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  • [Place of publication not identified] : Apress  (17)
  • [Erscheinungsort nicht ermittelbar] : Wiley
  • Artificial intelligence  (17)
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Language
Years
  • 1
    ISBN: 9781484290293 , 1484290291
    Language: English
    Pages: 1 online resource (253 pages) , illustrations (black and white, and colour).
    Parallel Title: Erscheint auch als
    Keywords: Artificial intelligence ; Python (Computer program language)
    Abstract: Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses. You will: Create code snippets and explain machine learning models using Python Leverage deep learning models using the latest code with agile implementations Build, train, and explain neural network models designed to scale Understand the different variants of neural network models.
    Note: Includes index. - Print version record
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  • 2
    ISBN: 9781484295021 , 1484295021
    Language: English
    Pages: 1 online resource (370 pages) , illustrations (black and white, and color).
    Parallel Title: Erscheint auch als
    Keywords: Artificial intelligence ; New business enterprises ; Intelligence artificielle ; Nouvelles entreprises ; artificial intelligence
    Abstract: Gain exclusive access to the secrets to building an enterprise AI start-up. AI innovation helps with every aspect of the business, from the supply chain, marketing, and advertising, customer service, risk management, operations to security. Industries from different verticals have been adopting AI and get real business values out of it. This book guides you through each step, from defining the business need and business model, all the way to registering IP and calculating your AI start-up valuation. You see how to perform market and technology validation, perform lean AI R&D, design AI architecture, AI product development and operationalization. The book also cover building and managing an AI team, along with attracting and keeping business and developer users, Building an Enterprise AI start-up is hard because Enterprise AI is an effort to build applications to mimic human intelligence to solve business problems. Hence it has a different challenge from building traditional non-AI applications, such as scouting, recruiting and managing AI talents; designing the most cost-efficient and scalable Enterprise AI; or establishing the best practice to operationalize AI in production As we are in the dawn of the AI-first product wave, AI-powered products for enterprises will be created for many years to come and AI Startup Strategy is the one-stop guide for it. You will: Match customers expectation VS technical feasibility Justify business values and ROI for customers Review the best business models for high valuation enterprise AI start-ups Design an AI product that gives a satisfactory experience for the user Register and value AI IP.
    Note: Includes index
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  • 3
    Online Resource
    Online Resource
    [Place of publication not identified] : Apress
    ISBN: 9781484289334 , 1484289331
    Language: English
    Pages: 1 online resource (1 video file (37 min.)) , sound, color.
    Edition: [First edition].
    DDC: 658/.05
    Keywords: Expert systems (Computer science) ; Artificial intelligence ; Computer software Development ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: This course is targeted toward novice or experienced developers and software architects and will help build a solid understanding of what Amazon Lex is capable of and what the best practices associated with it are. The author shares his experience in successfully building chatbots and what that success entailed. Part 3 of Building Bots with Amazon Lex begins with an introduction to the Amazon Lambda service. The author then dives into the details of how to connect Amazon Lex and Amazon Lambda services to extend the functionality of bots. He then shows how to test and deploy a bot with voice and text inputs. The course culminates with a demonstration of a virtual reality environment that is enhanced by chatbots. The course covers not only text-based but also delves into voice-based bots. After completing this course, you will be able to implement chatbots within your organization and define clear goals and objectives for stakeholders. What You Will Learn How to integrate with serverless functions on AWS How to validate and fulfill the user request How to publish a bot for user consumption How to test a bot with voice and text inputs Who This Video Is For Software developers, cloud developers, and cloud architects.
    Note: Online resource; title from title details screen (O'Reilly, viewed October 17, 2022)
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  • 4
    Online Resource
    Online Resource
    [Place of publication not identified] : Apress
    ISBN: 9781484288061 , 1484288068
    Language: English
    Pages: 1 online resource (1 video file (51 min.)) , sound, color.
    Edition: [First edition].
    DDC: 658/.05
    Keywords: Business Data processing ; Application software Development ; Artificial intelligence ; Human-computer interaction ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: This course will help you in your day to day journey with SAP. It covers how to find and use Parameter Id in SAP. Parameter Id personalizes all the daily transaction code and reports you are running and helps you to post and get data based on your requirement. You will learn about the usage of personal value list. This helps SAP users to store their data points inside SAP and use it whenever needed. This course also covers how to create layouts and variants so that you can get different reports and selection based on your daily requirement. This process helps tremendously in reducing error and confusion as you have all the data points pre built. Additionally, you will learn how you can default the values for certain fields in a transaction code so that the number of entries you have to key is much less and it's ready for automation. Finally, you can learn how to default the values in SAP FIORI apps which is the future of user experience and gives the flavor of buying in amazon shopping while running in SAP. This course will help you use SAP in a more productive way with less clicks and optimizing your work. This course will make you the driver that SAP needs it to operate. What you'll learn: how to create default layout and variants How to change your SAP themes and play with different SAP options Learn personal value list which will help you to move all your excel cheat sheet stored in SAP Study parameter Id which will help you to enter less duplicate stuff Who is this book for: Beginning to intermediate end users, SAP S/4HANA users, SAP ECC.
    Note: Online resource; title from title details screen (O'Reilly, viewed October 17, 2022)
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  • 5
    Online Resource
    Online Resource
    [Place of publication not identified] : Apress
    ISBN: 9781484289341 , 148428934X
    Language: English
    Pages: 1 online resource (1 video file (46 min.)) , sound, color.
    Edition: [First edition].
    DDC: 658/.05
    Keywords: Expert systems (Computer science) ; Artificial intelligence ; Computer software Development ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: This course is targeted toward both novice and experienced developers and software architects and will help build a solid understanding of what Amazon Lex is capable of and what the best practices associated with it are. The author shares his experience in successfully building chatbots and what that success entailed. Part 2 of Building Bots with Amazon Lex begins with an introduction to the Amazon Lex service. The author then dives into the details of creating intents, utterances, prompts, and context. This is a complete hands-on course, and the audience can easily map it with the basics covered in Part 1 of this vidoe series. Building Bots with Amazon Lex covers not only text-based but will also delve into voice-based bots. After completing this course, you will be able to implement chatbots within your organization and define clear goals and objectives for stakeholders. What You Will Learn How to configure Lex bot for multiple locales How to build a natural conversation on bots How to enable and use Sentiment Analysis with the bot How to enable and collect conversational logs from the bot Who This Video Is For Software developers, cloud developers, and cloud architects.
    Note: Online resource; title from title details screen (O'Reilly, viewed October 18, 2022)
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  • 6
    Online Resource
    Online Resource
    [Place of publication not identified] : Apress
    ISBN: 9781484287392 , 1484287398
    Language: English
    Pages: 1 online resource (1 video file (29 min.)) , sound, color.
    Edition: [First edition].
    DDC: 658/.05
    Keywords: Expert systems (Computer science) ; Artificial intelligence ; Computer software Development ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: This course is targeted towards novice or experienced developers and software architects. This course will help build a solid understanding of what Amazon Lex is capable of and what are the best practices associated with it. The author shares his experience on how he successfully built chatbots and what were the attributes for success. The course begins with an introduction to chatbot history, and terminologies like Persona, Utterances, Slots, and contexts. Next, it covers Amazon Lex with comparing chatbots between cloud providers, Amazon Lex history, and a use case on HR Bot. This is followed by describing the Life Cycle of the bot by explaining the Analysis phase activities, Design phase activities, and Development phase activities. There is a segment on Developing Business Logic for the bot. It concludes with discussing testing of the bot, deployment, and the channel for hosting the bot. The course will not only cover the text-based but will also delve into the voice-based bot. After completing this course, the audience will be able to visualize use cases where they can implement chatbots within their organization, define clear goals and objectives for the stakeholders. What you will learn What is Amazon Lex Understand life-cycle of a bot How to create your first bot How to host bot on Microsoft Teams Who this video is for Software developer, Cloud developers, Cloud Architects.
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  • 7
    ISBN: 9781484236796
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Microsoft Azure (Computing platform) ; Artificial intelligence ; Machine learning ; Electronic books ; local ; Electronic books
    Abstract: Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.
    Note: Includes bibliographical references. - Description based on online resource; title from cover (Safari, viewed September 14, 2018)
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  • 8
    ISBN: 9781484238080
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Artificial intelligence ; Human-computer interaction ; Business enterprises ; Technological innovations ; Electronic books ; local ; Electronic books
    Abstract: Get started with artificial intelligence in your business. This book will help you understand AI, its implications, and how to adopt a strategy that is rational, relevant, and practical. Beyond the buzzwords and the technology complexities, organizations are struggling to understand what AI means for their industry and how they can start their journey. How to Compete in the Age of Artificial Intelligence is not a book about complex formulas or solution architectures. It goes deeper into explaining the meaning and relevance of AI for your business. You will learn how to apply AI thinking across enterprise functions-including disruptive technologies such as IoT, Blockchain, and cloud-and transform your organization. What You'll Learn Know how to spot AI opportunities and establish the right organizational imperatives to grow your business Understand AI in the context of changing business dynamics and the workforce/skills required to succeed Discover how to apply AI thinking across enterprise functions-from the boardroom to cybersecurity, IoT, IT operations, policies-and implement a sustainable and integrated human-machine collaboration strategy Who This Book is For CxOs, senior executives, mid-level managers, AI evangelists, digital leads, and technology directors
    Note: Includes bibliographical references. - Description based on online resource; title from cover (Safari, viewed October 26, 2018)
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  • 9
    Online Resource
    Online Resource
    [Place of publication not identified] : Apress
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Artificial intelligence ; Application software ; Development ; Machine learning ; Natural language processing (Computer science) ; Electronic books ; Electronic books ; local
    Abstract: Want to build your first AI bot but don't know where to start? This book provides a comprehensive look at all the major bot frameworks available. You'll learn the basics for each framework in one place and get a clear picture for which one is best for your needs. Beginning AI Bot Frameworks starts with an overview of bot development and then looks at Google Wit.ai and APi.ai functions, IBM Watson, AWS bots with Lambda, FlockOS and TensorFlow. Additionally, it touches on Deep Learning and how bot frameworks can be extended to mixed reality with Hololens. By the end, you'll have mastered the different bot frameworks available and finally have the confidence to develop intelligent AI Chatbots of their own. What You'll Learn Review key structural points for building bots Understand the basic requirements for building a bot in each framework Integrate some of the frameworks Compare the features of each framework Who This Book Is For Computer Science students, engineers, developers and technology enthusiasts with some background in C#, node.js, and cloud platforms.
    Note: Description based on online resource; title from cover (Safari, viewed October 23, 2018)
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  • 10
    Online Resource
    Online Resource
    [Place of publication not identified] : Apress
    ISBN: 9781484234327
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Intelligent agents (Computer software) ; Artificial intelligence ; Electronic books ; local ; Electronic books
    Abstract: Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems. Building Intelligent Systems is based on more than a decade of experience building Internet-scale Intelligent Systems that have hundreds of millions of user interactions per day in some of the largest and most important software systems in the world. What You'll Learn Understand the concept of an Intelligent System: What it is good for, when you need one, and how to set it up for success Design an intelligent user experience: Produce data to help make the Intelligent System better over time Implement an Intelligent System: Execute, manage, and measure Intelligent Systems in practice Create intelligence: Use different approaches, including machine learning Orchestrate an Intelligent System: Bring the parts together throughout its life cycle and achieve the impact you want Who This Book Is For Software engineers, machine learning practitioners, and technical managers who want to build effective intelligent systems
    Note: Description based on online resource; title from cover (Safari, viewed March 21, 2018)
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  • 11
    Online Resource
    Online Resource
    [Place of publication not identified] : Apress
    ISBN: 9781484234235
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Artificial intelligence ; Electronic books ; local ; Electronic books
    Abstract: Discover the art and science of solving artificial intelligence problems with Python using optimization modeling. This book covers the practical creation and analysis of mathematical algebraic models such as linear continuous models, non-obviously linear continuous models, and pure linear integer models. Rather than focus on theory, Practical Python AI Projects , the product of the author's decades of industry teaching and consulting, stresses the model creation aspect; contrasting alternate approaches and practical variations. Each model is explained thoroughly and written to be executed. The source code from all examples in the book is available, written in Python using Google OR-Tools. It also includes a random problem generator, useful for industry application or study. What You Will Learn Build basic Python-based artificial intelligence (AI) applications Work with mathematical optimization methods and the Google OR-Tools (Optimization Tools) suite Create several types of projects using Python and Google OR-Tools Who This Book Is For Developers and students who already have prior experience in Python coding. Some prior mathematical experience or comfort level may be helpful as well.
    Note: Description based on online resource; title from cover (Safari, viewed March 14, 2018)
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  • 12
    Online Resource
    Online Resource
    [Place of publication not identified] : Apress
    ISBN: 9781484233573
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Artificial intelligence ; Machine learning ; C# (Computer program language) ; Generators (Computer programs) ; Electronic books ; local ; Electronic books
    Abstract: Discover how all levels Artificial Intelligence (AI) can be present in the most unimaginable scenarios of ordinary lives. This book explores subjects such as neural networks, agents, multi agent systems, supervised learning, and unsupervised learning. These and other topics will be addressed with real world examples, so you can learn fundamental concepts with AI solutions and apply them to your own projects. People tend to talk about AI as something mystical and unrelated to their ordinary life. Practical Artificial Intelligence provides simple explanations and hands on instructions. Rather than focusing on theory and overly scientific language, this book will enable practitioners of all levels to not only learn about AI but implement its practical uses. What You'll Learn Understand agents and multi agents and how they are incorporated Relate machine learning to real-world problems and see what it means to you Apply supervised and unsupervised learning techniques and methods in the real world Implement reinforcement learning, game programming, simulation, and neural networks Who This Book Is For Computer science students, professionals, and hobbyists interested in AI and its applications.
    Note: Description based on online resource; title from cover (Safari, viewed June 14, 2018)
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  • 13
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Microsoft .NET Framework ; Artificial intelligence ; Application program interfaces (Computer software) ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Get introduced to the world of artificial intelligence with this accessible and practical guide. Build applications that make intelligent use of language and user interaction to better compete in today's marketplace. Discover how your application can deeply understand and interpret content on the web or a user's machine, intelligently react to direct user interaction through speech or text, or make smart recommendations on products or services that are tailored to each individual user. With Microsoft Cognitive Services, you can do all this and more utilizing a set of easy-to-use APIs that can be consumed on the desktop, web, or mobile devices. Developers normally think of AI implementation as a tough task involving writing complex algorithms. This book aims to remove the anxiety by creating a cognitive application with a few lines of code. There is a wide range of Cognitive Services APIs available. This book focuses on some of the most useful and powerful ways that your application can make intelligent use of language. Artificial Intelligence for .NET: Speech, Language, and Search will show you how you can start building amazing capabilities into your applications today. What You'll Learn Understand the underpinnings of artificial intelligence through practical examples and scenarios Get started building an AI-based application in Visual Studio Build a text-based conversational interface for direct user interaction Use the Cognitive Services Speech API to recognize and interpret speech Look at different models of language, including natural language processing, and how to apply them in your Visual Studio application Reuse Bing search capabilities to better understand a user's intention Work with recommendation engines and integrate them into your apps Who This Book Is For Developers working on a range of platforms, from .NET and Windows to mobile devices. Examples are given in C#. No prior experience with AI techniques or theory is required.
    Note: Description based on online resource; title from cover (Safari, viewed November 14, 2018)
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  • 14
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Neural networks (Computer science) ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures. All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways. You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community. What You'll Learn Understand full stack deep learning using TensorFlow and gain a solid mathematical foundation for deep learning Deploy complex deep learning solutions in production using TensorFlow Carry out research on deep learning and perform experiments using TensorFlow Who This Book Is For Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts
    Note: Description based on online resource; title from cover (Safari, viewed May 22, 2018)
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  • 15
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: R (Computer program language) ; Artificial intelligence ; Machine learning ; Neural networks (Computer science) ; Electronic books ; Electronic books ; local
    Abstract: Understand deep learning, the nuances of its different models, and where these models can be applied. The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools. What You'll Learn Understand the intuition and mathematics that power deep learning models Utilize various algorithms using the R programming language and its packages Use best practices for experimental design and variable selection Practice the methodology to approach and effectively solve problems as a data scientist Evaluate the effectiveness of algorithmic solutions and enhance their predictive power Who This Book Is For Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.
    Note: Includes bibliographical references. - Description based on online resource; title from cover (Safari, viewed December 3, 2018)
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  • 16
    Online Resource
    Online Resource
    [Place of publication not identified] : Apress
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: MATLAB ; Machine learning ; Neural networks (Computer science) ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You'll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.
    Note: Includes bibliographical references. - Description based on online resource; title from cover (Safari, viewed September 29, 2017)
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  • 17
    Online Resource
    Online Resource
    [Place of publication not identified] : Apress
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Machine learning ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
    Note: Includes bibliographical references. - Description based on online resource; title from cover (Safari, viewed December 3, 2018)
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