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  • Sebastopol, CA : O'Reilly Media  (52)
  • Machine learning  (51)
  • Microcomputers ; Design and construction ; Amateurs' manuals
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  • 1
    Online Resource
    Online Resource
    Data science and engineering at enterprise scale : notebook-driven results and analysis (2019)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Information technology ; Management ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: As enterprise-scale data science sharpens its focus on data-driven decision making and machine learning, new tools have emerged to help facilitate these processes. This practical ebook shows data scientists and enterprise developers how the notebook interface, Apache Spark, and other collaboration tools are particularly well suited to bridge the communication gap between their teams. Through a series of real-world examples, author Jerome Nilmeier demonstrates how to generate a model that enables data scientists and developers to share ideas and project code. You'll learn how data scientists can approach real-world business problems with Spark and how developers can then implement the solution in a production environment. Dive deep into data science technologies, including Spark, TensorFlow, and the Jupyter Notebook Learn how Spark and Python notebooks enable data scientists and developers to work together Explore how the notebook environment works with Spark SQL for structured data Use notebooks and Spark as a launchpad to pursue supervised, unsupervised, and deep learning data models Learn additional Spark functionality, including graph analysis and streaming Explore the use of analytics in the production environment, particularly when creating data pipelines and deploying code
    Note: Description based on online resource; title from title page (Safari, viewed April 29, 2019)
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Deep learning for the life sciences : applying deep learning to genomics, microscopy, drug discovery, and more (2019)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Life sciences ; Data processing ; Machine learning ; Artificial intelligence ; Electronic books ; local ; Electronic books
    Abstract: Deep learning has already achieved remarkable results in many fields. Now it's making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You'll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine-an example that represents one of science's greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it's working
    Note: Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed April 24, 2019)
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Machine learning and security : protecting systems with data and algorithms (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Computer security ; Electronic books ; Electronic books ; local
    Abstract: Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you'll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions
    Note: Includes bibliographical references and index. - Description based on online resource; title from title page (viewed March 12, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781491979891/?ar
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Ethics and data science (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume)
    Edition: First edition.
    Keywords: Data mining ; Moral and ethical aspects ; Big data ; Machine learning ; Quantitative research ; Electronic books ; local
    Abstract: As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C's) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.
    Note: Description based on online resource; title from title page (Safari, viewed August 17, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492043898/?ar
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  • 5
    Online Resource
    Online Resource
    Artificial intelligence : the simplest way (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Artificial intelligence ; Machine learning ; Information technology ; Technological innovations ; Electronic books ; Electronic books ; local
    Abstract: Artificial intelligence is quickly moving from experimental to practical use. In the next few years, every business is going to need an AI strategy just to stay competitive. Getting access to AI technology is relatively straightforward; the hard part is applying the technology to a specific business context. This practical ebook guides you through the fundamental AI concepts and explains the simplest way for your organization to get started with this technology. Author Jerry Overton, a data scientist and fellow in DXC's Analytics Group, shows leaders in business, analytics, and IT how to choose an AI method, tie it to a specific business need, and then build, execute, and scale an AI solution to adapt to changing circumstances. You will learn ways to: Determine where AI can be useful in your organization by prioritizing areas that need improvement Create data stories that illustrate how AI can help bring about a solution Choose an AI strategy that produces practical results, using the scientific method as a guide Scale up by building and deploying utility services to make your AI available wherever it's needed Check your solution for bias and other ethical concerns by anticipating the AI's behavior
    Note: Description based on online resource; title from title page (Safari, viewed October 22, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492042075/?ar
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  • 6
    Online Resource
    Online Resource
    Achieving real business outcomes from artificial intelligence : enterprise considerations for AI initiatives (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Artificial intelligence ; Economic aspects ; Machine learning ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Artificial intelligence is already changing industry landscapes, with early adopters reporting benefits in high-value business cases such as fraud detection, preventative maintenance, and recommendation engines. Yet working on an AI initiative is demanding for many enterprises, whether you're in the middle of the process or just getting started. This ebook provides advice to help your company complete your AI journey. Chad Meley from Teradata and Atif Kureishy and Ben Mackenzie from Think Big Analytics provide countermeasures for common AI challenges that arise when creating a strategy, dealing with technical issues, or operationalizing an AI initiative. You'll explore several case studies, including how a major bank successfully used a variety of deep learning methods to fight financial crime. With this ebook, you'll discover: How deep learning has the potential to increase production, drive down cost, reduce waste, improve efficiency, and push innovation Options and trade-offs for leveraging AI capabilities, including SaaS solutions, public cloud-based APIs, and custom AI models AI case studies for mining image data, using image recognition, providing customer service, and designing document automation How to overcome challenges in delivering value from custom AI development What to do in the face of emerging AI trends over the next three years
    Note: Description based on online resource; title from title page (Safari, viewed January 17, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492038214/?ar
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    MPI Ethno. Forsch.
  • 7
    Online Resource
    Online Resource
    Hands-on unsupervised learning using Python : how to discover hidden patterns in unlabeled data (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Python (Computer program language) ; Machine learning ; Artificial intelligence ; Electronic books ; local ; Electronic books
    Abstract: Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 1, 2019)
    URL: lizenzpflichtig
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  • 8
    Online Resource
    Online Resource
    An introduction to machine learning interpretability : an applied perspective on fairness, accountability, transparency, and explainable AI (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Innovation and competition are driving analysts and data scientists toward increasingly complex predictive modeling and machine learning algorithms. This complexity makes these models accurate but also makes their predictions difficult to understand. When accuracy outpaces interpretability, human trust suffers, affecting business adoption, regulatory oversight, and model documentation. Banking, insurance, and healthcare in particular require predictive models that are interpretable. In this ebook, Patrick Hall and Navdeep Gill from H2O.ai thoroughly introduce the idea of machine learning interpretability and examine a set of machine learning techniques, algorithms, and models to help data scientists improve the accuracy of their predictive models while maintaining interpretability. Learn how machine learning and predictive modeling are applied in practice Understand social and commercial motivations for machine learning interpretability, fairness, accountability, and transparency Explore the differences between linear models and more accurate machine learning models Get a definition of interpretability and learn about the groups leading interpretability research Examine a taxonomy for classifying and describing interpretable machine learning approaches Learn several practical techniques for data visualization, training interpretable machine learning models, and generating explanations for complex model predictions Explore automated approaches for testing model interpretability
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed April 17, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492033158/?ar
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  • 9
    Online Resource
    Online Resource
    Solving quality and maintenance problems with AI : combining machine learning, deep learning, and associative memory reasoning to improve operations (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Artificial intelligence ; Machine learning ; Information technology ; Management ; Electronic books ; Electronic books ; local
    Abstract: Despite all the hype and money surrounding artificial intelligence ($1.2 trillion in 2017 alone), many companies aren't certain this "technology revolution" can help them solve current business needs. This report explores real-world business use cases that demonstrate how machine learning, deep learning, and associative memory reasoning together can provide lucrative returns today for companies across several industries in the area of predictive quality and maintenance (PQM). In this report, you'll learn how a combination of cutting-edge approaches-known as complimentary learning-can help your company increase uptime, reduce risk, stop over-maintenance of assets, and fix defects sooner. Interviews with companies spanning industries, including Accenture, Keystone, and Intel, demonstrate how these AI-powered PQM solutions impact businesses today. You'll explore: AI-based PQM solutions that make up a large and growing segment of the overall AI applications market How associative memory reasoning mimics the human's ability to learn, memorize and reason, surfacing the hidden connection in data How complementary learning combines several AI systems, including machine learning, deep learning, and cognitive computing How Accenture and Intel have used Intel Saffron technology to solve specific issues related to PQM
    Note: Description based on online resource; title from title page (Safari, viewed July 3, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781491999561/?ar
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  • 10
    Online Resource
    Online Resource
    Machine learning with Python cookbook : practical solutions from preprocessing to deep learning (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You'll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models
    Note: Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed March 19, 2018)
    URL: lizenzpflichtig
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  • 11
    Online Resource
    Online Resource
    Applied text analysis with Python : enabling language-aware data products with machine learning (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Python (Computer program language) ; Natural language processing (Computer science) ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist's approach to building language-aware products with applied machine learning. You'll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you'll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity
    Note: Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed July 23, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781491963036/?ar
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  • 12
    Online Resource
    Online Resource
    Best practices for bringing AI to the enterprise (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume)
    Edition: First edition.
    Keywords: Artificial intelligence ; Machine learning ; Business enterprises ; Data processing ; Electronic books ; Electronic books ; local
    Abstract: How do you determine if AI is the right solution for your business problem? This quick guide provides you with a checklist of 10 best practices for bringing AI into the enterprise. It starts with three basic questions for managers, CxOs, and engineers/architects to help determine if an AI solution might be the right choice for the problems their organizations face: Is the task genuinely data-driven? Do you have the data to perform the task? Do you need the scale that automation provides? If you answer yes to all three, these best practices can help you plan an AI effort to solve the problem. Developed by Kristian Hammond, Chief Scientist at Narrative Science and professor of Computer Science and Journalism at Northwestern University, this high-level checklist will guide you toward the development of systems that reflect your needs, are supported by the right data, and are integrated with your workflow and your people.
    Note: Description based on online resource; title from title page (Safari, viewed February 13, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492029762/?ar
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  • 13
    Online Resource
    Online Resource
    Modernizing cybersecurity operations with machine intelligence : advanced threat detection, hunting, and analysis (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Computer networks ; Security measures ; Machine learning ; Computer crimes ; Prevention ; Information technology ; Management ; Electronic books ; Electronic books ; local
    Abstract: Adversaries and hackers have gained significant and distinct advantages in cyber warfare today. Creative, fast, and opportunistic attackers have created an ecosystem of advanced persistent threats that is growing in scale and complexity, and evolving more rapidly than our capabilities to respond. By integrating machine intelligence (MI), you can significantly modernize your organization's security operations to better keep pace with these threats. With this ebook, Peter Guerra and Paul Tamburello-chief executives at Booz Allen Hamilton-provide examples to show you how MI can change cybersecurity operations to be more effective and efficient in threat detection, monitoring, and risk analysis. Applying MI to automate cybersecurity processes will enable swifter and more accurate identification of new and emerging threats in this continually changing landscape. Attacks are only likely to increase in size and frequency across companies, institutions, and government agencies. This ebook demonstrates a powerful tool that can help you level the playing field. You will learn: The scope of the existing threat landscape, and benefits of applying MI to cybersecurity What machine intelligence can and cannot do when applied to cybersecurity Specific security applications of machine intelligence in the real world How to address your organization's maturity and readiness for cybersecurity with MI The first steps for moving ahead with MI security in your organization How to overcome difficulties and challenges when adopting machine intelligence
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 9, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492035992/?ar
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  • 14
    Online Resource
    Online Resource
    Python vs. R for data science (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume)
    Edition: First edition.
    Keywords: Python (Computer program language) ; R (Computer program language) ; Data mining ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Python and R are two of the mainstream languages in data science. Fundamentally, Python is a language for programmers, whereas R is a language for statisticians. In a data science context, there is a significant degree of overlap when it comes to the capabilities of each language in the fields of regression analysis and machine learning. Your choice of language will depend highly on the environment in which you are operating. In a production environment, Python integrates with other languages much more seamlessly and is therefore the modus operandi in this context. However, R is much more common in research environments due to its more extensive selection of libraries for statistical analysis.
    Note: "Comparisons"--Cover. - Description based on online resource; title from title page (Safari, viewed July 24, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492033943/?ar
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  • 15
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    Online Resource
    Security with AI and machine learning : using advanced tools to improve application security at the edge (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Artificial intelligence ; Computer networks ; Security measures ; Computer security ; Electronic books ; Electronic books ; local
    Abstract: For security professionals seeking reliable ways to combat persistent threats to their networks, there's encouraging news. Tools that employ AI and machine learning have begun to replace the older rules- and signature-based tools that can no longer combat today's sophisticated attacks. In this ebook, Oracle's Laurent Gil and Recorded Future's Allan Liska look at the strengths (and limitations) of AI- and ML-based security tools for dealing with today's threat landscape. This high-level overview demonstrates how these new tools use AI and ML to quickly identify threats, connect attack patterns, and allow operators and analysts to focus on their core mission. You'll also learn how managed security service providers (MSSPs) use AI and ML to identify patterns from across their customer base. This ebook explains: Why rules-based, signature-based, and firewall solutions have fallen short How automated bots enable cybercriminals and nation-state actors to attack your network The evolution of the botnet: how threat actors constantly change their attack strategy How AI and ML techniques in web applications help you observe, quantify, and classify inbound requests How to detect insider threats and advanced persistent threat actors with AI and ML tools Case studies that show how a media company, an airline, and a university use AL and ML in security
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 19, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492043133/?ar
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  • 16
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    Machine learning is changing the rules : ways business can utilize AI to innovate (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Artificial intelligence ; Business enterprises ; Technological innovations ; Electronic books ; Electronic books ; local
    Abstract: We live in a time of massive market disruption. On top of the long-running computer revolution, the business world is now faced with artificial intelligence, machine learning, and deep learning-part of the emerging fourth industrial revolution. This in-depth ebook provides practical advice for organizations looking to launch a machine-learning initiative, and explores use cases for six industries involved in AI and machine learning today. Author Peter Morgan, CEO of Data Science Partnership, takes you through three primary requirements for machine learning: sophisticated learning algorithms, dedicated hardware, and large datasets. Companies with big data strategies have already satisfied one condition, but any organization can jump into machine learning through a variety of open source and proprietary solutions. This ebook guides you through several options. You'll explore: How machine learning is transforming healthcare, finance, transportation, computer technology, energy, and science Use cases including self-driving cars, software development, genomics, blockchains, algorithmic trading, particle physics, and data center energy management Open source datasets and proprietary data sources for organizations that don't generate their own unique data A typical data science life cycle, from data collection to production and scale Examples of commercial off-the-shelf (COTS) and open source machine-learning solutions-and the pros and cons of each Open source deep learning frameworks such as TensorFlow, MXnet, and PyTorch AI as a Service providers including AWS, Google Cloud Platform, Azure, and IBM Cloud Disruptive technologies that are just beginning to emerge
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed July 26, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492035367/?ar
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  • 17
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    Understanding patterns of disruption : lessons learned from the cloud, machine learning, and more (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Entrepreneurship ; Success in business ; Technological innovations ; Cloud computing ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: From startups to established market leaders, many companies today are taking advantage of disruptive forces to produce new products, become more efficient, or demolish barriers to entry. The web is a good example of a disruptive force, but subtler forms of disruption such as new tools or a fresh take on old ideas can also give your business a much needed boost. In this ebook, Q. McCallum and Ken Gleason take you through several disruption examples and provide a framework to help you spot a disruption opportunity in the wild. Whether it's applying an existing technology in a new way, creating a new business model or market segment, or launching a value network that invites other companies to provide resources or consume your service, this ebook will guide you through the discovery process. Look at two major disruptions from the 1990s that businesses now take for granted Explore how successful businesses such as Uber created market niches by mixing several technologies in clever ways Learn eight ways to spot disruption opportunities, including underserved markets, sources of waste, and artificial barriers to entry Dive into detailed examples of disruption today: the cloud, predictive analytics and machine learning, multi-sided markets, and self-publishing
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 31, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492027225/?ar
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  • 18
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    Feature engineering for machine learning : principles and techniques for data scientists (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you'll learn techniques for extracting and transforming features-the numeric representations of raw data-into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You'll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques
    Note: Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed March 11, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781491953235/?ar
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  • 19
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    TensorFlow for deep learning : from linear regression to reinforcement learning (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Reinforcement learning ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Learn how to solve challenging machine learning problems with TensorFlow, Google's revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. It's ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms. Learn TensorFlow fundamentals, including how to perform basic computation Build simple learning systems to understand their mathematical foundations Dive into fully connected deep networks used in thousands of applications Turn prototypes into high-quality models with hyperparameter optimization Process images with convolutional neural networks Handle natural language datasets with recurrent neural networks Use reinforcement learning to solve games such as tic-tac-toe Train deep networks with hardware including GPUs and tensor processing units
    Note: Includes index. - Description based on online resource; title from title page (Safari, viewed April 24, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781491980446/?ar
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    Deep learning cookbook : practical recipes to get started quickly (2018)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Deep learning doesn't have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you'll learn how to solve deep-learning problems for classifying and generating text, images, and music. Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you're stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks. You'll learn how to: Create applications that will serve real users Use word embeddings to calculate text similarity Build a movie recommender system based on Wikipedia links Learn how AIs see the world by visualizing their internal state Build a model to suggest emojis for pieces of text Reuse pretrained networks to build an inverse image search service Compare how GANs, autoencoders and LSTMs generate icons Detect music styles and index song collections
    Note: Includes index. - Description based on online resource; title from title page (Safari, viewed March 11, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781491995839/?ar
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  • 21
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    Artificial intelligence and machine learning in industry : perspectives from leading practitioners (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume)
    Edition: First edition.
    Keywords: Artificial intelligence ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: The growth of businesses centered on artificial intelligence and machine learning make it clear: automation will fundamentally reshape industry and society. But this will only happen after a broader sweep and scrutiny of AI and its economic, social, and political influence, from scholars and policymakers alike. This report adds to this discussion through interviews with the entrepreneurs and executives on the front lines of AI, machine learning, and industry. Among the interviews: Michael Osborne explores the coming effects of automation, based on his work as Dyson Associate Professor in Machine Learning at Oxford Arjun Singh, cofounder and CEO of Gradescope, talks about AI in education with "Helping Students Learn with Machine Learning" Jake Heller, founder and CEO of Casetext, takes us on a tour of machine learning and the law in "The Future of Legal Practice" Aaron Kimball, CTO of Zymergen, illuminates the hidden world of "Intelligent Microbes" and their commercial use Bryce Meredig, Chief Science Officer of Citrine Informatics, describes ways to apply machine learning for inventing new materials in "The Periodic Table as Training Data" Erik Andrejko discusses his work at The Climate Corporation and the role machine learning plays in farming and agriculture
    Note: Description based on online resource; title from title page (viewed January 10, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048916/?ar
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  • 22
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    Learning TensorFlow : a guide to building deep learning systems (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: TensorFlow (Electronic resource) ; Machine learning ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience-from data scientists and engineers to students and researchers. You'll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems in TensorFlow. Get up and running with TensorFlow, rapidly and painlessly Learn how to use TensorFlow to build deep learning models from the ground up Train popular deep learning models for computer vision and NLP Use extensive abstraction libraries to make development easier and faster Learn how to scale TensorFlow, and use clusters to distribute model training Deploy TensorFlow in a production setting
    Note: Includes index. - Description based on online resource; title from title page (Safari, viewed August 21, 2017)
    URL: lizenzpflichtig
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  • 23
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    Data science with Java : practical methods for scientists and engineers (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Java (Computer program language) ; Machine learning ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today's data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java. You'll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you'll find code examples you can use in your applications. Examine methods for obtaining, cleaning, and arranging data into its purest form Understand the matrix structure that your data should take Learn basic concepts for testing the origin and validity of data Transform your data into stable and usable numerical values Understand supervised and unsupervised learning algorithms, and methods for evaluating their success Get up and running with MapReduce, using customized components suitable for data science algorithms
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed June 12, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491934104/?ar
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  • 24
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    Introduction to GPUs for data analytics : advances and applications for accelerated computing (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Graphics processing units ; Programming ; Real-time data processing ; Machine learning ; Big data ; Electronic books ; Electronic books ; local
    Abstract: Moore's law has finally run out of steam for CPUs. The number of x86 cores that can be placed cost-effectively on a single chip has reached a practical limit, making higher densities prohibitively expensive for most applications. Fortunately, for big data analytics, machine learning, and database applications, a more capable and cost-effective alternative for scaling compute performance is already available: the graphics processing unit, or GPU. In this report, executives at Kinetica and Sierra Communications explain how incorporating GPUs is ideal for keeping pace with the relentless growth in streaming, complex, and large data confronting organizations today. Technology professionals, business analysts, and data scientists will learn how their organizations can begin implementing GPU-accelerated solutions either on premise or in the cloud. This report explores: How GPUs supplement CPUs to enable continued price/performance gains The many database and data analytics applications that can benefit from GPU acceleration Why GPU databases with user-defined functions (UDFs) can simplify and unify the machine learning/deep learning pipeline How GPU-accelerated databases can process streaming data from the Internet of Things and other sources in real time The performance advantage of GPU databases in demanding geospatial analytics applications How cognitive computing-the most compute-intensive application currently imaginable-is now within reach, using GPUs
    Note: Description based on online resource; title from title page (Safari, viewed January 9, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781491998045/?ar
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  • 25
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    Getting started with artificial intelligence : a practical guide to building enterprise applications (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Artificial intelligence ; Machine learning ; Application software ; Development ; Natural language processing (Computer science) ; Information technology ; Technological innovations ; Electronic books ; Electronic books ; local
    Abstract: Artificial intelligence techniques are becoming a fundamental component of business growth across a wide range of industries. Enterprise technologies that automate and detect patterns can augment human expertise, empowering both employees and applications to make richer, more data-driven decisions. Complete with case studies, implementation examples, and a survey of the current landscape, this book serves as an ideal starting point for your journey into enterprise AI. As a developer tasked with building enterprise-level AI applications, you're looking to learn about the tools and techniques that will help you get the job done. In this book, authors Tom Markiewicz and Josh Zheng-developer advocates for IBM Watson-explore three of the more common uses for AI in the enterprise: natural language processing (NLP), computer vision, and chatbots.You'll also learn methods for creating a data pipeline that forms the backbone for building these applications. Explore why NLP is the key to mining unstructured data in emails, articles, blog posts, customer support discussions, and other documents Gain practical tips for building chatbots, and learn how these applications are used in the customer support and e-commerce industries Examine capabilities that computer vision brings to your applications, including image classification and tagging Get a high-level guide to data pipelines-the backbone of your AI applications
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 8, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492027805/?ar
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    Considering TensorFlow for the enterprise : an overview of the deep learning ecosystem (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: TensorFlow (Electronic resource) ; Machine learning ; Artificial intelligence ; Electronic data processing ; Management ; Business enterprises ; Data processing ; Electronic books ; Electronic books ; local
    Abstract: Deep learning is enabling the next generation of successful companies. The question is no longer whether enterprises will use deep learning (they will), but how involved each organization becomes with the technology. Sean Murphy and Allen Leis introduce deep learning from an enterprise perspective and offer an overview of the TensorFlow library and ecosystem. If your company is adopting deep learning, this report will help you navigate the initial decisions you must make-from choosing a deep learning framework to integrating deep learning with the other data analysis systems already in place-to ensure you're building a system capable of handling your specific business needs. Explore fundamental concepts and core questions about deep learning in the enterprise Familiarize yourself with available framework options, including TensorFlow, MXNet, Microsoft Cognitive Toolkit, and Deeplearning4J Dive into TensorFlow's library and ecosystem, from tools such as estimators, prebuilt neural networks, Keras, ML Toolkit for TensorFlow, Tensor2Tensor (T2T), TensorBoard, and TensorFlow Debugger, to model deployment and management with TensorFlow Serving See how companies such as Jet.com and PingThings have implemented deep learning to improve the accuracy and enhance the performance of a number of tasks
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 9, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781491995075/?ar
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  • 27
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    Machine learning logistics : model management in the real world (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Information technology ; Management ; Business enterprises ; Data processing ; Management ; Electronic books ; Electronic books ; local
    Abstract: To succeed with machine learning or deep learning, you must handle the logistics well. Simply put, you need an effective management system for overall data flow and the evaluation and deployment of multiple models as they move from prototype to production. Without that, your project will most likely fail. This report examines what you need for effective data and model management in real-world settings, including globally distributed cloud or on-premises systems. Authors Ted Dunning and Ellen Friedman introduce the rendezvous architecture, an innovative design to help you handle machine-learning logistics. This approach not only paves the way to successful long-term management, it also frees up your time and effort to focus on the machine learning process itself and on how to take action on results. This report provides a basic, non-technical view of what makes the approach work, as well as in-depth technical details. The report is ideal for data scientists, architects, developers, ops teams, and project managers, whether your team is planning to build a machine learning system, or currently has one underway. You will learn: The issues in machine learning logistics you need to consider when designing and implementing your system How the rendezvous architecture leverages streaming data, provides hot hand-off of new models, and collects diagnostic data Practical tips for comparing live models, including the role of decoys, canaries and the t-digest Best practices for maintaining performance after deployment
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 9, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781491997628/?ar
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    Thoughtful machine learning with Python : a test-driven approach (2017)
    Sebastopol, CA : O'Reilly Media
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    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python's Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you're a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms
    Note: Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed January 27, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491924129/?ar
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    Practical machine learning with H2O : powerful, scalable techniques for deep learning and AI (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Artificial intelligence ; Computer simulation ; Electronic books ; Electronic books ; local
    Abstract: Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that's easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. If you're familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets.
    Note: Includes index. - Description based on online resource; title from title page (viewed January 4, 2017)
    URL: lizenzpflichtig
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    Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Graphics in this book are printed in black and white . Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and TensorFlow-author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details
    Note: Includes index. - Description based on online resource; title from title page (Safari, viewed March 22, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491962282/?ar
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    Deep learning : a practitioner's approach (2017)
    Sebastopol, CA : O'Reilly Media
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    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Artificial intelligence ; Machine learning ; Neural networks (Computer science) ; Electronic books ; Electronic books ; local
    Abstract: Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning-especially deep neural networks-make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J's workflow tool Learn how to use DL4J natively on Spark and Hadoop
    Note: Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed August 7, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491924570/?ar
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    Artificial intelligence now : current perspectives from O'Reilly Media (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Artificial intelligence ; Machine learning ; Information technology ; Technological innovations ; Electronic books ; Electronic books ; local
    Abstract: A collection of posts from the O'Reilly AI blog. Cf. Introduction.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 3, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492049210/?ar
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    The path to predictive analytics and machine learning (2017)
    Sebastopol, CA : O'Reilly Media
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    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Data mining ; Artificial intelligence ; Management information systems ; Electronic books ; Electronic books ; local
    Abstract: In many companies today, discussions about predictive analytics and machine learning tend to overlook one critical component: implementation. This report will help you examine practical methods for building and deploying scalable, production-ready machine-learning applications. Leveraging machine-learning models in production, after all, separates revenue generation and cost savings from mere intellectual novelty. Product specialists from MemSQL describe several real-time use cases, including "operational" applications, where machine-learning models automate decision-making processes, as well as "interactive" applications, where machine learning informs decisions made by humans. You'll also explore modern data processing architectures and leading technologies available for data processing, analysis, and visualization. With this report, you'll find ways to: Build real-time data pipelines Process transactions and analytics in a single database Create custom real-time dashboards Redeploy batch models in real time Build real-time machine learning applications Prepare data pipelines for predictive analytics and machine learning Apply predictive analytics to real-time challenges Use techniques for predictive analytics in production Move from machine learning to artificial intelligence
    Note: Description based on online resource; title from title page (Safari, viewed June 12, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492042884/?ar
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    Serving machine learning models : a guide to architecture, stream processing engines, and frameworks (2017)
    Sebastopol, CA : O'Reilly Media
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    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Artificial intelligence ; Business enterprises ; Technological innovations ; Electronic books ; Electronic books ; local
    Abstract: Model serving is a critical but often underappreciated aspect of machine learning.Once you have built a model using your training data set, you need to packageand deploy (i.e., serve) it. It's a surprisingly complex task, in part because modeltraining is usually handled by data scientists, and model serving is the domain ofsoftware engineers. These two groups have different functions, concerns, andtools, so the handoff can be tricky. Plus, machine learning is a hot and fast-growing field, spawning a slew of new tools that require software engineers tocreate new model serving frameworks. This book delves into the theory and practice of serving machine learning modelsin streaming applications. It proposes an overall architecture that implementscontrolled streams of both data and models that enables not only real-time modelserving, as part of processing input streams, but also real-time model updating. Italso covers: Step-by- step options for exporting models in tensorflow and PMMLformats. Implementation of model serving leveraging stream processing enginesand frameworks including Apache Flink, Apache Spark streaming, ApacheBeam, Apache Kafka streams, and Akka streams. Monitoring approaches for model serving implementations.
    Note: Description based on online resource; title from title page (Safari, viewed January 10, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492024095/?ar
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    Fundamentals of deep learning : designing next-generation machine intelligence algorithms (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Artificial intelligence ; Machine learning ; Neural networks (Computer science) ; Maschinelles Lernen ; Deep learning
    Abstract: With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that's paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you're familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Understand the fundamentals of reinforcement learning
    Note: Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed June 6, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781491925607/?ar
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    Data science in the cloud with Microsoft Azure machine learning and Python (2016)
    Sebastopol, CA : O'Reilly Media
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    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Windows Azure ; Python (Computer program language) ; Cloud computing ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Azure ML, Microsoft's Azure Machine Learning cloud platform, provides an easy-to-use and powerful set of cloud-based data transformation and machine-learning tools. With this O'Reilly report, you'll learn the basics of manipulating data and constructing and evaluating models in Azure ML, illustrated by a complete data science example. Author Stephen Elston from Quantia Analytics performs the required data manipulation throughout the report, and then constructs and evaluates a regression model for a bicycle-sharing-demand dataset. You can follow along by downloading the code and data provided. You'll then learn how to publish your trained models as web services in the Azure cloud. This report will show you how: Azure ML provides a powerful environment for the creation and cloud deployment of predictive analytic solutions R code is readily integrated into the Azure ML workflow Careful development, selection, and filtering of features is the key to most data science problems Recognizing business goals and requirements is essential to creating a successful data science solution A complete understanding of residuals is essential to the evaluation of predictive model performance
    Note: Description based on online resource; title from title page (viewed January 9, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781491917176/?ar
    URL: https://learning.oreilly.com/library/view/-/9781492042594/?ar
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    Machine learning for designers (2016)
    Sebastopol, CA : O'Reilly Media
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    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; User interfaces (Computer systems) ; Electronic books ; Electronic books ; local
    Abstract: Machine learning is no longer just a tool for data scientists. By taking advantage of recent advances in this technology, UI and UX designers can find ways to better engage with and understand their users. This O'Reilly report not only introduces you to contemporary machine learning systems, but also provides a conceptual framework to help you integrate machine-learning capabilities into your user-facing designs. Using tangible, real-world examples, author Patrick Hebron explains how machine-learning applications can affect the way you design websites, mobile applications, and other software. You'll learn how recent advancements in machine learning can radically enhance software capabilities through natural language processing, image recognition, content personalization, and behavior prediction. This report explains how to: Leverage machine-generated user insights to provide a more personalized customer or user experience Spot opportunities for the integration of machine-learning capabilities into existing designs and platforms Choose the right machine-learning platforms or services Design for the probabilistic and often imprecise nature of machine-generated data Stay up to date with advancements in the field and spot emerging opportunities for machine learning-aided design
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed August 11, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781491971444/?ar
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    The future of machine intelligence : perspectives from leading practitioners (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume)
    Edition: First edition.
    Keywords: Machine learning ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Advances in both theory and practice are throwing the promise of machine learning into sharp relief. The field has the potential to transform a range of industries, from self-driving cars to intelligent business applications. Yet machine learning is so complex and wide-ranging that even its definition can change from one person to the next. The series of interviews in this exclusive report unpack concepts and innovations that represent the frontiers of ever-smarter machines. You'll get a rare glimpse into this exciting field through the eyes of some of its leading minds. In these interviews, these ten practitioners and theoreticians cover the following topics: Anima Anandkumar: high-dimensional problems and non-convex optimization Yoshua Bengio: Natural Language Processing and deep learning Brendan Frey: deep learning meets genomic medicine Risto Miikkulainen: the startling creativity of evolutionary algorithms Ben Recht: a synthesis of machine learning and control theory Daniela Rus: the autonomous car as a driving partner Gurjeet Singh: using topology to uncover the shape of your data Ilya Sutskever: the promise of unsupervised learning and attention models Oriol Vinyals: sequence-to-sequence machine learning Reza Zadeh: the evolution of machine learning and the role of Spark
    Note: Description based on online resource; title from title page (Safari, viewed June 11, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492042334/?ar
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    Practical artificial intelligence in the Cloud : exploring AI-as-a-service for business and research (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    ISBN: 9781491967393 , 1491967390
    Language: English
    Pages: 1 online resource (1 volume) , illustration
    Keywords: Artificial intelligence ; Economic aspects ; Machine learning ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Only a couple of years ago, artificial intelligence was a cliché, a sad remnant of 1950s-style futurism. Today it's sexy again, and that far-off future of intelligent bots is just around the corner. Consider the latest trend, AI-as-a-Service (AIaaS). Software vendors such as Google, IBM, and Amazon are rolling out sophisticated cloud-based AI and machine-learning services for a growing market of developers and users in business and academia. Through interviews with consumers and executives of AIaaS vendors, author Mike Barlow examines the primary driver of this new approach: AI is simply too big for any single device or system. But with AIaaS, developers can build applications that perform data collection and compression on devices, while advanced processes such as natural language processing and machine learning are performed in the cloud. When will consumers feel the impact? Fairly soon, you may be dealing with customer-service bots that know the answers before you even ask a question. Download this report and explore how: AI is now a distributed phenomenon, with information passed back and forth between local devices and remote systems AIaaS vendors already support a diverse menu of APIs for AI developers. AI apps and interfaces will be designed and engineered increasingly for non-technical users Companies will routinely incorporate AI capabilities into new products and services
    Note: Description based on online resource; title from title page (viewed January 3, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492049814/?ar
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    The evolution of analytics : opportunities and challenges for machine learning in business (2016)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume)
    Edition: First edition.
    Keywords: Machine learning ; Artificial intelligence ; Business enterprises ; Computer networks ; Management ; Information technology ; Management ; Electronic books ; Electronic books ; local
    Abstract: Machine learning is a hot topic in business. Even data-driven organizations that have spent years developing successful data analysis platforms, with many accurate statistical models in place, are now looking into this decades-old discipline. But how can companies turn hyped opportunities for machine learning into real business value? This report examines the growing momentum of machine learning in the analytics landscape, the challenges machine learning presents to businesses, and examples of how organizations are actively seeking to incorporate modern machine learning techniques into their production data infrastructures. Authors Patrick Hall, Wen Phan, and Katie Whitson look at two companies in depth-one in healthcare and one in finance-that are seeing the real impact of machine learning. Discover how machine learning can help your organization: Analyze and generate insights from large amounts of varied, messy, and unstructured data unfit for traditional statistical analysis Increase the predictive accuracy beyond what was previously possible Augment aging analytical processes and other decision-making tools
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed June 11, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492042570/?ar
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    Data science in the cloud with Microsoft Azure machine learning and R : 2015 update (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Windows Azure ; R (Computer program language) ; Cloud computing ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Take some time to explore Microsoft's Azure machine learning platform, Azure ML-a production environment that simplifies the development and deployment of machine learning models. In this updated and expanded O'Reilly report, Stephen Elston from Quantia Analytics uses a complete data science example (forecasting hourly demand for a bicycle rental system) to show you how to manipulate data, construct models, and evaluate models with Azure ML. The report walks you through key steps in the data science process from problem definition, data understanding, and feature engineering, through construction of a regression model and presentation of results. You'll also learn how to extend Azure ML with R. Elston uses downloadable sample R code and data to demonstrate how to perform data munging, data visualization, and in-depth evaluation of model performance. At the end, you'll learn how to publish your trained models as web services in the Azure cloud. With this 2015 Update, you'll learn how to: Navigate the Azure ML Gallery Use the R Model module Load R packages from a zip file Use the Metadata Editor Publish a scoring model as a web service Use the Cross Validate model module Publish a web service to Excel Apply a SQL transformation Use the new Sweep Parameters module
    Note: Description based on online resource; title from title page (viewed January 4, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492049906/?ar
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  • 42
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    Evaluating machine learning models : a beginner's guide to key concepts and pitfalls (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Data science today is a lot like the Wild West: there's endless opportunity and excitement, but also a lot of chaos and confusion. If you're new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming. Now you have help. With this O'Reilly report, machine-learning expert Alice Zheng takes you through the model evaluation basics. In this overview, Zheng first introduces the machine-learning workflow, and then dives into evaluation metrics and model selection. The latter half of the report focuses on hyperparameter tuning and A/B testing, which may benefit more seasoned machine-learning practitioners. With this report, you will: Learn the stages involved when developing a machine-learning model for use in a software application Understand the metrics used for supervised learning models, including classification, regression, and ranking Walk through evaluation mechanisms, such as hold?out validation, cross-validation, and bootstrapping Explore hyperparameter tuning in detail, and discover why it's so difficult Learn the pitfalls of A/B testing, and examine a promising alternative: multi-armed bandits Get suggestions for further reading, as well as useful software packages
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed December 6, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781492048756/?ar
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  • 43
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    Analyzing and visualizing data with F# (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: F♯ (Computer program language) ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: In this report, F# contributor Tomas Petricek explains many of the key features of the F# language that make it a great tool for data science and machine learning. Real world examples take you through the entire data science workflow with F#, from data access and analysis to presenting the results. You'll learn about: How F# and its unique features-such as type providers-ease the chore of data access The process of data analysis and visualization, using the Deedle library, R type provider and the XPlot charting library Implementations for a clustering algorithm using the standard F# library and how the F# type inference helps you understand your code The report also includes a list of resources to help you learn more about using F# for data science.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 8, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048350/?ar
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  • 44
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    Data and social good : using data science to improve lives, fight injustice, and support democracy (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume)
    Edition: First edition.
    Keywords: Electronic data processing ; Moral and ethical aspects ; Machine learning ; Big data ; Humanitarianism ; Electronic books ; Electronic books ; local
    Abstract: Data may indeed be the "new oil"-a seemingly inexhaustible source of fuel for spectacular economic growth-but it's also a valuable resource for humanitarian groups looking to improve and protect the lives of less fortunate people. In this O'Reilly report, you'll learn how statisticians and data scientists are volunteering their time to help a variety of nonprofit organizations around the world. Mike Barlow cites several examples of how data and the work of data scientists have made a measurable impact on organizations such as DataKind, a group that connects socially minded data scientists with organizations working to address critical humanitarian issues. There's certainly no lack of demand for data science services among nonprofits today, because these organizations, too, realize the potential of data for changing people's fortunes.
    Note: Description based on online resource; title from title page (Safari, viewed January 7, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048213/?ar
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  • 45
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    Oil, gas, and data : high-performance data tools in the production of industrial power (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume)
    Edition: First edition.
    Keywords: Petroleum industry and trade ; Statistics ; Gas industry ; Statistics ; Big data ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Oil and gas companies have been dealing with large amounts of data much longer than most industries, and some energy analysts even refer to it as the "original big data industry." Now, with massive increases of seismic data, advances in network-attached devices, and a vast quantity of historical data on paper, the oil and gas space also presents one of today's most complex data science problems. As this O'Reilly report reveals, the industry is working to add machine learning and predictive analytics in all phases of its exploration, production, refinement, and delivery operations. But it's still in the early adoption phase. While oil and gas has embraced the 'digital oilfield' concept, it's a cautious IT culture, with many companies waiting to see what others do first. In this report, you'll learn how: Big data solutions from other industries can't be easily applied to oil and gas Much innovation is in the discovery and exploration phase, where risk and uncertainty are high Outside companies such as Hortonworks, SparkBeyond, and WellWiki are making a difference Oil companies now run some of the largest private supercomputing facilities in the world Security tools such as rapid detection are important to an industry with memories of the Stuxnet worm and Shamoon virus
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 7, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048206/?ar
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    Learning Spark : lightening fast data analysis (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: ApacheSpark ; Big data ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3 , this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates.
    Note: Includes index. - Description based on online resource; title from cover page (Safari, viewed February 10, 2015)
    URL: https://learning.oreilly.com/library/view/-/9781449359034/?ar
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    Predictive maintenance : a world of zero unplanned downtime (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume)
    Edition: First edition.
    Keywords: Information technology ; Management ; Machine learning ; Electronic digital computers ; Maintenance and repair ; Electronic books ; Electronic books ; local
    Abstract: Predictive maintenance isn't a new idea. For decades, people in heavy industry have dreamt of eliminating costly machine breakdowns and unplanned system downtime. Now that the data revolution has finally made predictive maintenance feasible, the possibilities seem endless. This O'Reilly report examines how the combination of advanced analytics, low-cost sensors, and the IoT promises to elevate maintenance from a cost center to a profit center. Through interviews with several industry experts, award-winning journalist Mike Barlow reveals that predictive maintenance is more than merely a tool or solution- it's an integrated business strategy. Plant managers can run equipment longer at less cost, VPs can optimize operations in general, and C-suite executives can turn those efficiencies into market advantages and real profits. Many IT, manufacturing, healthcare, and energy firms already practice predictive maintenance, but companies in other industries need time and expertise to pursue the large-scale data collection, advanced analysis, machine learning, and automation required to make it work. This report explores the possible rewards and caveats of this innovative model. Mike Barlow is an award-winning journalist, author, communications strategy consultant, and cofounder of Cumulus Partners.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 7, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048183/?ar
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    Real world active learning : applications and strategies for human-in-the-loop machine learning (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Human-computer interaction ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Machine learning isn't a set-it-and-forget-it operation. Even with solid examples, ML algorithms can still fail and end up blocking important emails, filtering out useful content, and causing a variety of other problems. In this O'Reilly report, industry analyst Ted Cuzzillo examines real-world examples of active learning, a relatively new strategy for improving ML results through short-term human intervention. Throughout this report, Cuzzillo relies on several experts in the field for practical applications and tips they've unearthed through various projects in active learning. As you'll discover, the point at which algorithms fail is precisely where there's an opportunity to insert human judgment to actively improve the algorithm's performance. Learn the basic principles of labeling data Help algorithms decrease the uncertainty of their results in email spam filtering and online search accuracy Effectively use crowdsourcing in your ML training project, without incurring undue costs Select training data from areas where the data volume is greatest Use multiple classification methods to better train ML algorithms Learn how to apply more advanced strategies for even greater accuracy
    Note: Description based on online resource; title from title page (Safari, viewed January 7, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048176/?ar
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    The last mile of analytics (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Decision making ; Data processing ; Machine learning ; Big data ; Electronic books ; Electronic books ; local
    Abstract: Here's the net takeaway: Businesses want insights from data they can translate into meaningful actions and real results. Software vendors are beginning to deliver a new generation of advanced analytics packages that address business issues directly. In this O'Reilly report, Mike Barlow reveals how this new user-friendly software is helping businesses go beyond data analysis and straight to decision-making-without requiring data science expertise or truckloads of cash. How has advanced analytics progressed from lab project to commercial product so quickly? Through interviews with data analysts, you'll understand the role that machine learning plays in specialized analytics packages, and how this software alone can make decisions based on what's likely to happen next. When you have these capabilities, you've reached "the last mile of analytics."
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 7, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492048084/?ar
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    Thoughtful machine learning : a test-driven approach (2015)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 v.) , ill.
    Keywords: Machine learning ; Machine learning ; Testing ; Electronic books ; Electronic books ; local
    Abstract: Learn how to apply test-driven development (TDD) to machine-learning algorithms-and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks.
    Note: Includes bibliographical references and index. - Description based on online resource; title from cover (Safari, viewed Oct. 10, 2014)
    URL: lizenzpflichtig
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    Practical machine learning : a new look at anomaly detection (2014)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 v.) , ill.
    Keywords: Machine learning ; Anomaly detection (Computer security) ; Electronic books ; Electronic books ; local
    Abstract: Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may not know what the puzzle is, much less what "suspects" you're looking for. This O'Reilly report uses practical example to explain how the underlying concepts of anomaly detection work.
    Note: Description based on online resource; title from title page (Safari, viewed Aug. 29, 2014)
    URL: https://learning.oreilly.com/library/view/-/9781491914151/?ar
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    Building the perfect PC (2004)
    Sebastopol, CA : O'Reilly Media | Boston, MA :Safari,
    Details
    Language: English
    Pages: xvi, 332 p. , ill. ; , 25 cm
    Edition: 1st ed.
    DDC: 621.3916
    Keywords: IBM-compatible computers ; Design and construction ; Amateurs' manuals ; Microcomputers ; Design and construction ; Amateurs' manuals ; Electronic books ; local
    Abstract: With off-the-shelf computer systems to fit any budget or requirement, some people might wonder why you'd want to build your own. They don't understand that for many computer users, a ready-made system is about as satisfying as popping a frozen dinner in the microwave when you'd rather have real food instead. Sure, it works, but it's not exactly what you need or want. There's a lot to be said for a computer that has exactly the quality components you choose, by the manufacturers you choose, rather than the conveniently bundled assortment made for your price point. But perhaps you've stopped short of building your own because you're inexperienced or you're concerned it won't come out right. After all, you want your computer to be perfect. Hesitate no longer: Building the Perfect PC delivers end-to-end instructions (that even inexperienced PC-builders can follow) for creating your ideal machine. Written by hardware experts Robert Bruce Thompson and Barbara Fritchman Thompson, this book covers it all, beginning with the various reasons why you'd want to build your own PC, planning and picking out the components, and putting it all together. The book outlines a variety of complete systems and all of their components, including a small form factor PC; a middle-of-the-road "sweet spot" system with top-notch components; and a top-end gaming PC with high-end video, a fast processor, and enough RAM to make your palms sweat just thinking about it. Straightforward language, clear directions, and extensive illustrations make this guide a breeze for computer builders of any level to follow--even those with little or no experience. If you've yearned for a quality machine with high-end components that suit your needs, then Building the Perfect PC will put you where you want to be: in control of your computer system.
    Note: Includes index
    URL: https://learning.oreilly.com/library/view/-/0596006632/?ar
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