Abstract: 27+ Hours of Video Instruction An outstanding data scientist or machine learning engineer must master more than the basics of using ML algorithms with the most popular libraries, such as scikit-learn and Keras. To train innovative models or deploy them to run performantly in production, an in-depth appreciation of machine learning theory is essential, which includes a working understanding of the foundational subjects of linear algebra, calculus, probability, statistics, data structures, and algorithms. When the foundations of machine learning are firm, it becomes easier to make the jump from general ML principles to specialized ML domains, such as deep learning, natural language processing, machine vision, and reinforcement learning. The more specialized the application, the more likely its implementation details are available only in academic papers or graduate-level textbooks, either of which assume an understanding of the foundational subjects. This master class includes the following courses: Linear Algebra for Machine Learning Calculus for Machine Learning LiveLessons Probability and Statistics for Machine Learning Data Structures, Algorithms, and Machine Learning Optimization Linear Algebra for Machine Learning LiveLessons provides you with an understanding of the theory and practice of linear algebra, with a focus on machine learning applications. Calculus for Machine Learning LiveLessons introduces the mathematical field of calculus,Äîthe study of rates of change,Äîfrom the ground up. It is essential because computing derivatives via differentiation is the basis of optimizing most machine learning algorithms, including those used in deep learning, such as backpropagation and stochastic gradient descent. Probability and Statistics for Machine Learning (Machine Learning Foundations) LiveLessons provides you with a functional, hands-on understanding of probability theory and statistical modeling, with a focus on machine learning applications. Data Structures, Algorithms, and Machine Learning Optimization LiveLessons provides you with a functional, hands-on understanding of the essential computer science for machine learning applications. About the Instructor Jon Krohn is Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the industry,Äôs most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at Columbia University, New York University, leading industry conferences, via O'Reilly, and via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010; his papers have been cited more than a thousand times. Course Requirements Mathematics: Familiarity with secondary school-level mathematics will make the course easier to follow. If you are comfortable dealing with quantitative information,Äîsuch as understanding charts and rearranging simple equations,Äîthen you should be well-prepared to follow along with all of the mathematics. Programming: All code demos are in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples. About Pearson Video Training Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que. Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Abstract: 3 Hours of Video Discover the astounding state-of-the-art in Natural Language Processing (NLP) that is enabled by Large Language Models (LLMs) like ChatGPT and T5 Understand Attention and Transformers, as well as how these essential modern NLP concepts relate to Deep Learning and LLMs Survey the staggeringly broad range of LLMs' natural-language capabilities Learn how to use LLMs in practice, including how to train and deploy them into production NLP applications Large Language Models (LLMs) such as GPT series architectures have dramatically accelerated the natural language processing (NLP) capabilities of machines in recent years. These capabilities, facilitated by LLMs' hundreds of billions of model parameters, approach or exceed human-level performance on a staggeringly broad set of natural-language tasks -- often without any task-specific training being required. In this event, leading subject-matter experts introduce LLMs and their associated concepts (e.g., Transformers, Attention), survey LLMs' breadth of capabilities, and provide the best practices on how to leverage LLMs efficiently and confidently in order to supercharge your own natural-language applications. AI Catalyst The AI Catalyst Conference from Pearson brings together leading voices in AI to make complex topics understandable and actionable. Host Jon Krohn guides the conversation and explains how to bring state-of-the-art methods into practice. Gain new information or a different perspective to make an impact in your job and in the world. By the end of the course, you'll understand: Large Language Models (LLMs) Attention Transformers The breadth of state-of-the-art NLP applications And you'll be able to: Select an appropriate LLM architecture for a given NLP application Prompt pre-trained LLMs like ChatGPT and GPT-3 to effectively produce your desired output Train and deploy LLMs into production NLP applications Potentially accelerate your data science roadmap by months or years by leveraging a pre-trained LLM instead of needing to train individual task-specific models from scratch yourself This course is for you because... You'd like to appreciate the staggering breadth of NLP and Deep Learning capabilities You are a data scientist, software developer, ML engineer, or other technical professional who would like to be able incorporate new NLP approaches into real-world applications Prerequisites All you need is an interest in how AI can impact you and your organization. Recommended Follow-up Read: Quick Start Guide to LLMs by Sinan Ozdemir, https://learning.oreilly.com/library/view/quick-start-guide/9780138199425/ Attend: Deploying GPT and Large Language Models by Sinan Ozdemir: https://learning.oreilly.com/search/?q=Sinan%20Ozdemir&type=live-event-series&rows=10&publishers=Pearson Attend: Hands-on Natural Language Generation and GPT by Sinan Ozdemir: https://learning.oreilly.com/search/?q=Sinan%20Ozdemir&type=live-event-series&rows=10&publishers=Pearson Read: Chapter 15 of Learning Deep Learning by Dr. Magnus Ekman: https://learning.oreilly.com/library/view/learning-deep-learning/9780137470198/ Watch: NLP using Transformer Architectures by Aur©♭lien G©♭ron: https://learning.oreilly.com/videos/natural-language-processing/0636920373605/0636920373605-video329383/ For a more general introduction to deep learning, check out the Deep Learning: The Complete Guide playlist by Dr. Jon Krohn: https://learning.oreilly.com/playlists/a40ea8fe-994d-4370-8b29-0d6c0f519a89/ Course Schedule Jon Krohn: Welcome Sinan Ozdemir: Introduction to Large Language Models (30 minutes) We can't talk about state-of-the-art Natural Language Processing (NLP) without talking about Transformers and large language models (LLMs) like ChatGPT, BERT, GPT, and T5. Sinan explores a brief history of modern NLP up to the rise of attention-based models and Transformers including the proliferation of LLMs that continues to this day along with all of the good and sometimes the not-so-good outcomes. He overviews the major architectures that influence the tasks and models that dominate NLP while peeking under the hood to understand how LLMs learn to read, write, and do so much more. Sinan Ozdemir is an active lecturer focusing on large language models and a former lecturer of data science at the Johns Hopkins University. He is the author of multiple textbooks on data science and machine learning including The Principles of Data Science. Sinan is the Founder and CTO of LoopGenius where he uses State of the art AI to help people create and run their businesses. He holds a master's degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco. Jon and Sinan Discussion + Q&A Melanie Subbiah: The Broad Range of LLM Capabilities Large language models have unlocked a huge number of exciting applications in the real world that were not possible before -- capabilities that are creative, useful, and profitable. Through interactive demos of GPT-3, Melanie explores a broad range of these use cases, giving participants more intuition for how large language models have been effective. Melanie Subbiah is a third-year PhD student in NLP at Columbia University where she researches narrative summarization and aspects of online text safety. Before starting graduate school, she was one of the lead authors on the GPT-3 paper, building out the evaluation suite for that work and helping early customers use the OpenAI API for their projects. Prior to that, she researched autonomous systems at Apple. Melanie obtained her Bachelor's in computer science from Williams College. Jon and Melanie Discussion + Q&A Shaan Khosla: Training and Deploying LLMs Shaan covers practical LLM tips over the full NLP lifecycle. These include topics such as efficient training practices, validation methods, and productionization considerations to ensure your design is optimized for implementation within your real-world natural-language application. Shaan Khosla is a data scientist at Nebula where he researches, designs, and develops NLP models. He's previously worked at Bank of America on an internal machine learning consulting team, where he used LLMs to build proof of concept systems for various lines of business. Shaan holds a BSBA in Computer Science and Finance from the University of Miami and is currently completing a master's degree in Data Science at NYU. He has published multiple peer-reviewed papers applying LLMs, topic modeling, and recommendation systems to the fields of biochemistry and healthcare. Jon and Shaan Discussion + Q&A Jon Krohn: Closing Remarks About the Host Host: Jon Krohn is Co-Founder and Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the data science industry's most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at leading universities and conferences, as well as via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.

Abstract: "Deep Reinforcement Learning and GANs (Generative Adversarial Networks) LiveLessons is an introduction to two of the most exciting topics in Deep Learning today. Generative Adversarial Networks cast two Deep Learning networks against each other in a "forger-detective" relationship, enabling the fabrication of stunning, photorealistic images with flexible, user-specifiable elements. Deep Reinforcement Learning has produced equally surprising advances, including the bulk of the most widely-publicized "artificial intelligence" breakthroughs. Deep RL involves training an "agent" to become adept in given "environments," enabling algorithms to meet or surpass human-level performance on a diverse range of complex challenges, including Atari video games, the board game Go, and subtle hand-manipulation tasks. Throughout these lessons, essential theory is brought to life with intuitive explanations and interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow, the most popular Deep Learning library."--Resource description page.

Abstract: Sneak Peek The Sneak Peek program provides early access to Pearson video products and is exclusively available to Safari subscribers. Content for titles in this program is made available throughout the development cycle, so products may not be complete, edited, or finalized, including video post-production editing.

Abstract: 7+ Hours of Video Instruction An intuitive, application-focused introduction to deep learning and TensorFlow, Keras, and PyTorch Overview Deep Learning with TensorFlow, Keras, and PyTorch LiveLessons is an introduction to deep learning that brings the revolutionary machine-learning approach to life with interactive demos from the most popular deep learning library, TensorFlow, and its high-level API, Keras, as well as the hot new library PyTorch. Essential theory is whiteboarded to provide an intuitive understanding of deep learning's underlying foundations; i.e., artificial neural networks. Paired with tips for overcoming common pitfalls and hands-on code run-throughs provided in Python-based Jupyter notebooks, this foundational knowledge empowers individuals with no previous understanding of neural networks to build powerful state-of-the-art deep learning models. About the Instructor Jon Krohn is the Chief Data Scientist at the machine learning company untapt. He presents a popular series of tutorials published by Addison-Wesley and is the author of the acclaimed book Deep Learning Illustrated . Jon teaches his deep learning curriculum in-classroom at the New York City Data Science Academy. He holds a doctorate in neuroscience from Oxford University, lectures at Columbia University, and carries out machine vision research at Columbia's Irving Medical Center. Skill Level Intermediate Learn How To Build deep learning models in all the major libraries: TensorFlow, Keras, and PyTorch Understand the language and theory of artificial neural networks Excel across a broad range of computational problems including machine vision, natural language processing, and reinforcement learning Create algorithms with state-of-the-art performance by fine-tuning model architectures Self-direct and complete your own Deep Learning projects Who Should Take This Course Software engineers, data scientists, analysts, and statisticians with an interest in deep learning. Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful. Previous experience with statistics or machine learning is not necessary. Course Requirements Some experience with any of the following are an asset, but none are essential: Object-oriented programming, specifically Python Simple shell commands; e.g., in Bash Machine learning or statistics Lesson Descriptions Lesson 1: Introduction to Deep Learning and Artifi...

Abstract: "Deep Learning with TensorFlow LiveLessons is an introduction to Deep Learning that bring the revolutionary machine-learning approach to life with interactive demos from the most popular Deep Learning library, TensorFlow, and its high-level API, Keras. Essential theory is whiteboarded to provide an intuitive understanding of Deep Learning's underlying foundations, i.e., artificial neural networks. Paired with tips for overcoming common pitfalls and hands-on code run-throughs provided in Python-based Jupyter notebooks, this foundational knowledge empowers individuals with no previous understanding of neural networks to build powerful state-of-the-art Deep Learning models."--Resource description page.

Abstract: "An intuitive introduction to processing natural language data with Deep Learning models Deep Learning for Natural Language Processing LiveLessons is an introduction to processing natural language with Deep Learning. These lessons bring intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow, the most popular Deep Learning library. In the early lessons, specifics of working with natural language data are covered, including how to convert natural language into numerical representations that can be readily processed by machine learning approaches. In the later lessons, state-of-the art Deep Learning architectures are leveraged to make predictions with natural language data."--Resource description page.

Abstract: Sneak Peek The Sneak Peek program provides early access to Pearson video products and is exclusively available to Safari subscribers. Content for titles in this program is made available throughout the development cycle, so products may not be complete, edited, or finalized, including video post-production editing. 6 Hours of Video Instruction An intuitive introduction to the latest superhuman capabilities facilitated by Deep Learning. Overview Machine Vision, GANs, Deep Reinforcement Learning LiveLessons is an introduction to three of the most exciting topics in Deep Learning today. Modern machine vision involves automated systems outperforming humans on image recognition, object detection, and image segmentation tasks. Generative Adversarial Networks cast two Deep Learning networks against each other in a “forger-detective” relationship, enabling the fabrication of stunning, photorealistic images with flexible, user-specifiable elements. Deep Reinforcement Learning has produced equally surprising advances, including the bulk of the most widely-publicized “artificial intelligence” breakthroughs. Deep RL involves training an “agent” to become adept in given “environments,” enabling algorithms to meet or surpass human-level performance on a diverse range of complex challenges, including Atari video games, the board game Go, and subtle hand-manipulation tasks. Throughout these lessons, essential theory is brought to life with intuitive explanations and interactive, hands-on Jupyter notebook demos. Examples feature Python and straightforward Keras layers in TensorFlow 2, the most popular Deep Learning library. About the Instructor Jon Krohn is Chief Data Scientist at the machine learning company untapt. He presents a popular series of deep learning tutorials published by Addison-Wesley and is the author of the bestselling book Deep Learning Illustrated . Jon teaches his deep learning curriculum in-classroom at the New York City Data Science Academy, as well as guest lecturing at Columbia University and New York University. He holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading journals since 2010. Skill Level Intermediate Learn How To Understand the high-level theory and key language around machine vision, deep reinforcement learning, and generative adversarial networks Create state-of-the art models for image recognition, object detection, and image segmentation Architect GANs that crea...

Abstract: Das Buch bietet einen einfachen Zugang zum Aufbau von Deep-Learning-Modellen und erleichtert das Lernen mit farbenfrohen, lebendigen Illustrationen. Teil I erklärt, was Deep Learning ist, warum es so allgegenwärtig geworden ist und wie es sich auf Konzepte und Terminologien wie Künstliche Intelligenz, Machine Learning, Künstliche Neuronale Netze und Verstärkungslernen bezieht. Die einleitenden Kapitel sind vollgepackt mit anschaulichen Illustrationen, leicht verständlichen Analogien und charakterorientierten Erzählungen. Auf dieser Grundlage bieten die Autoren eine praktische Referenz und ein Tutorial zur Anwendung eines breiten Spektrums bewährter Techniken des Deep Learning. Die wesentliche Theorie wird mit so wenig Mathematik wie möglich behandelt und mit Python-Code beleuchtet. Die Theorie wird durch praktische »Durchläufe« unterstützt, die kostenfrei online verfügbar sind (Jupyter-Notebooks) und ein pragmatisches Verständnis aller wichtigen Deep-Learning-Ansätze und ihrer Anwendungen liefern: Machine Vision, Natural Language Processing, Bilderzeugung und Spielalgorithmen.

Abstract: 6.5 Hours of Video Instruction An introduction to the linear algebra behind machine learning models Overview Linear Algebra for Machine Learning LiveLessons provides you with an understanding of the theory and practice of linear algebra, with a focus on machine learning applications. About the Instructor Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated (Addison-Wesley, 2020), an instant #1 bestseller that has been translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University and New York University, as well as online via O'Reilly, YouTube, and the Super Data Science Podcast. Jon holds a PhD from Oxford and has been publishing on machine learning in leading academic journals since 2010; his papers have been cited over a thousand times. Skill Level Intermediate Learn How To Appreciate the role of algebra in machine and deep learning Understand the fundamentals of linear algebra, a ubiquitous approach for solving for unknowns within high-dimensional spaces Develop a geometric intuition of what's going on beneath the hood of machine learning algorithms, including those used for deep learning Be able to more intimately grasp the details of machine learning papers as well as all of the other subjects that underlie ML, including calculus, statistics, and optimization algorithms Manipulate tensors of all dimensionalities including scalars, vectors, and matrices, in all of the leading Python tensor libraries: NumPy, TensorFlow, and PyTorch Reduce the dimensionality of complex spaces down to their most informative elements with techniques such as eigendecomposition (eigenvectors and eigenvalues), singular value decomposition, and principal components analysis Who Should Take This Course Users of high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms who would now like to understand the fundamentals underlying the abstractions, enabling them to expand their capabilities Software developers who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems Data scientists who would like to reinforce their understanding of the subjects at the core of their professional discipline Data analysts or AI enthusiasts who would like to become a data scientist or data/ML engineer and are keen to deeply und...