{"title":"Machine Learning \u0026 AI","description":"\u003ch2\u003eMachine Learning \u0026amp; AI Books: Deep Learning \u0026amp; Data Science\u003c\/h2\u003e\n\u003cp\u003eExplore our machine learning and AI books collection. The definitive deep learning and machine learning books for students, engineers and data scientists. Find Goodfellow's Deep Learning, Hands-On Machine Learning with Scikit-Learn, Keras \u0026amp; TensorFlow, and Deep Learning with Python.\u003cbr\u003e \u003cbr\u003eBrowse our machine learning books online below. All with free standard shipping across the US.\u003c\/p\u003e","products":[{"product_id":"ace-the-data-science-interview-201-real-question-answers","title":"Ace the Data Science Interview: 201 Real Interview Questions (0578973839)","description":"\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eBuy Ace the Data Science Interview Online\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eAce the Data Science Interview (ISBN-13 9780578973838\u003c\/strong\u003e\u003cspan\u003e\u003cstrong\u003e)\u003c\/strong\u003e by \u003c\/span\u003e\u003cstrong\u003eNick Singh\u003c\/strong\u003e\u003cspan\u003e and \u003c\/span\u003e\u003cstrong\u003eKevin Huo\u003c\/strong\u003e\u003cspan\u003e is the \u003c\/span\u003e\u003cstrong\u003edata science interview prep\u003c\/strong\u003e\u003cspan\u003e book built around the actual questions companies ask, not the questions authors guess they ask. It collects 201 real \u003c\/span\u003e\u003cstrong\u003edata science interview questions\u003c\/strong\u003e\u003cspan\u003e drawn from \u003c\/span\u003e\u003cstrong\u003eFAANG\u003c\/strong\u003e\u003cspan\u003e companies, tech startups, and Wall Street quantitative firms, with worked solutions that explain not just what the answer is but how to reason your way there under interview pressure.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eCurrently 33% off the original retail price, this is the clearest, most direct path to structured \u003c\/span\u003e\u003cspan\u003edata science interview preparation\u003c\/span\u003e\u003cspan\u003e available in a single paperback. If you have a \u003c\/span\u003e\u003cstrong\u003edata scientist interview\u003c\/strong\u003e\u003cspan\u003e coming up, this is the book to read first.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eAbout This Data Science Interview Book\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eMost \u003c\/span\u003e\u003cspan\u003edata science interview preparation\u003c\/span\u003e\u003cspan\u003e resources give you concepts to study. This one gives you the questions you will actually face. The authors, \u003c\/span\u003e\u003cspan\u003eNick Singh\u003c\/span\u003e\u003cspan\u003e and \u003c\/span\u003e\u003cspan\u003eKevin Huo\u003c\/span\u003e\u003cspan\u003e, are former data scientists at major tech and finance firms, and they built this book from their own interview experiences and from collecting questions from candidates who went through real data science interviews at Google, Meta, Amazon, and comparable companies.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe result is a \u003c\/span\u003e\u003cspan\u003edata science interview book\u003c\/span\u003e\u003cspan\u003e that covers every area of the modern \u003c\/span\u003e\u003cspan\u003edata scientist interview\u003c\/span\u003e\u003cspan\u003e in one place: \u003c\/span\u003e\u003cspan\u003estatistics\u003c\/span\u003e\u003cspan\u003e, \u003c\/span\u003e\u003cspan\u003eSQL\u003c\/span\u003e\u003cspan\u003e, Python, \u003c\/span\u003e\u003cspan\u003emachine learning\u003c\/span\u003e\u003cspan\u003e, product sense, and case questions. The \u003c\/span\u003e\u003cspan\u003eAce The Data Science Interview\u003c\/span\u003e\u003cspan\u003e approach is organized around the \u003c\/span\u003e\u003cspan\u003edata science interview process\u003c\/span\u003e\u003cspan\u003e the way companies actually run it, not the way a textbook would cover the underlying subjects. That difference matters when you have a specific interview date and limited prep time.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWhat the 201 Data Science Interview Questions Cover\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eStatistics\u003c\/span\u003e\u003cspan\u003e and probability: distributions, hypothesis testing, A\/B testing, and the statistical reasoning data scientists use daily\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eSQL\u003c\/span\u003e\u003cspan\u003e interview questions: query writing, joins, aggregations, window functions, and schema design questions asked in technical rounds\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003ePython and coding: data manipulation, algorithm questions, and the coding challenges that appear in data science technical screens\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eMachine learning data science interview questions\u003c\/span\u003e\u003cspan\u003e: model selection, evaluation metrics, bias-variance tradeoff, and real-world ML problem framing\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eTechnical data science interview questions\u003c\/span\u003e\u003cspan\u003e: system design for ML, data pipeline questions, and applied modeling scenarios\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eProduct sense questions: metrics, experiment design, and how to analyze the impact of a product change using data\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eCase and business questions: translating a business problem into a data science problem and communicating your approach clearly\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eInterview strategy: how to approach each question type, what interviewers look for, and how to structure your answers\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eKey Features of Ace the Data Science Interview\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003e201 real \u003c\/span\u003e\u003cstrong\u003edata science interview questions\u003c\/strong\u003e\u003cspan\u003e from \u003c\/span\u003e\u003cstrong\u003eFAANG\u003c\/strong\u003e\u003cspan\u003e, startups, and Wall Street firms\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eWorked solutions with reasoning, not just answers\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eCovers all six areas of the \u003c\/span\u003e\u003cstrong\u003edata scientist interview\u003c\/strong\u003e\u003cspan\u003e: stats, \u003c\/span\u003e\u003cstrong\u003eSQL\u003c\/strong\u003e\u003cspan\u003e, Python, ML, product, and case\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eWritten by \u003c\/span\u003e\u003cstrong\u003eNick Singh\u003c\/strong\u003e\u003cspan\u003e and \u003c\/span\u003e\u003cstrong\u003eKevin Huo\u003c\/strong\u003e\u003cspan\u003e, former data scientists at top tech and finance companies\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eThe most focused \u003c\/span\u003e\u003cstrong\u003eFAANG Interview Prep Book\u003c\/strong\u003e\u003cspan\u003e for data science candidates available\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003e301 pages, paperback, \u003c\/span\u003e\u003cstrong\u003eISBN-13 9780578973838\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e    \u003c\/span\u003e\u003cspan\u003e Free standard US shipping included\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWho Should Read This Data Science Interview Prep Book?\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThis book is for anyone who is actively preparing for a \u003c\/span\u003e\u003cspan\u003edata scientist interview\u003c\/span\u003e\u003cspan\u003e at a technology company, financial firm, or data-driven startup. Candidates targeting \u003c\/span\u003e\u003cspan\u003eFAANG\u003c\/span\u003e\u003cspan\u003e companies use it to understand the difficulty and format of questions before their technical rounds. Candidates from non-traditional backgrounds use it to identify and fill the specific gaps in their interview preparation before they waste time on general study.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eAnalytics engineers, business intelligence professionals, and software engineers moving into data science roles use it to understand where \u003c\/span\u003e\u003cspan\u003edata science interview\u003c\/span\u003e\u003cspan\u003es differ from the interviews they have already passed. Data science graduate students heading into their first industry job search use it alongside their coursework to bridge the gap between academic training and the \u003c\/span\u003e\u003cspan\u003edata science interview process\u003c\/span\u003e\u003cspan\u003e that companies actually run. If your goal is a \u003c\/span\u003e\u003cspan\u003edata scientist interview\u003c\/span\u003e\u003cspan\u003e at a serious company, this book covers what you need to know.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWhy Choose Ace the Data Science Interview?\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe \u003c\/span\u003e\u003cspan\u003edata science interview process\u003c\/span\u003e\u003cspan\u003e is genuinely different from a software engineering interview, and most general prep resources do not reflect that. They cover coding or \u003c\/span\u003e\u003cspan\u003emachine learning\u003c\/span\u003e\u003cspan\u003e separately but miss the \u003c\/span\u003e\u003cspan\u003estatistics\u003c\/span\u003e\u003cspan\u003e and product sense components that data science interviewers consistently test. This \u003c\/span\u003e\u003cspan\u003edata science interview preparation\u003c\/span\u003e\u003cspan\u003e book is the one that covers all six areas in one place, with real questions rather than invented practice problems.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe other reason this book works is that \u003c\/span\u003e\u003cspan\u003eNick Singh\u003c\/span\u003e\u003cspan\u003e and \u003c\/span\u003e\u003cspan\u003eKevin Huo\u003c\/span\u003e\u003cspan\u003e explain the reasoning, not just the answers. An interview answer you understand well enough to adapt in the moment is worth more than ten memorized answers that break down when the interviewer follows up. That is the approach that makes this \u003c\/span\u003e\u003cspan\u003edata science interview prep\u003c\/span\u003e\u003cspan\u003e resource stand out.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eFrequently Asked Questions\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: What is Ace the Data Science Interview about?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: It is a data science interview prep book by Nick Singh and Kevin Huo containing 201 real data science interview questions asked at FAANG companies, tech startups, and Wall Street firms, with worked solutions and strategy for each question type.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: What is the ISBN-13 for Ace the Data Science Interview?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: The ISBN-13 is 9780578973838 (ISBN-10: 0578973839).\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: What question categories does the book cover?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: The book covers statistics and probability, SQL, Python and coding, machine learning, product sense, and case and business questions, all areas that appear in real data science interviews at major companies.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Is this book suitable for data science beginners?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: It is best suited for candidates who already have foundational data science knowledge and are preparing for job interviews. It is not an introductory textbook but a focused data science interview preparation guide.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Does this cover FAANG interview questions?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: Yes. The 201 questions in the book are drawn from real interviews at FAANG companies (Meta, Apple, Amazon, Netflix, Google), as well as tech startups and Wall Street quantitative firms.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003eFor support, contact \u003ca href=\"mailto:support@mindabooks.com\"\u003esupport@mindabooks.com\u003c\/a\u003e.\u003c\/p\u003e","brand":"MindaBooks","offers":[{"title":"Default Title","offer_id":43199993839691,"sku":null,"price":46.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0687\/9769\/4027\/files\/Acethedatescienceinterview.jpg?v=1789982320"},{"product_id":"hands-on-machine-learning-scikit-learn-tensorflow-3rd-edition","title":"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 3rd Edition (1098125975)","description":"\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eBuy Hands-On Machine Learning Online\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eHands On Machine Learning\u003c\/strong\u003e\u003cspan\u003e with \u003c\/span\u003e\u003cstrong\u003eScikit-Learn\u003c\/strong\u003e\u003cspan\u003e, \u003c\/span\u003e\u003cstrong\u003eKeras\u003c\/strong\u003e\u003cspan\u003e, and \u003c\/span\u003e\u003cstrong\u003eTensorFlow\u003c\/strong\u003e\u003cspan\u003e, 3rd Edition \u003cstrong\u003e(\u003c\/strong\u003e\u003c\/span\u003e\u003cstrong\u003eISBN-13 9781098125974\u003c\/strong\u003e\u003cspan\u003e\u003cstrong\u003e)\u003c\/strong\u003e by \u003c\/span\u003e\u003cstrong\u003eAurélien Géron\u003c\/strong\u003e\u003cspan\u003e is the practical machine learning guide that engineers and data scientists actually build projects from. It covers machine learning with Scikit-Learn for classical algorithms and deep learning with Keras and TensorFlow for neural networks, all in one well-organized, code-first book that respects your time.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eCurrently 48% off retail price, this is one of the best-value machine learning books available right now. \u003c\/span\u003e\u003cspan\u003eBuy Hands On Machine Learning\u003c\/span\u003e\u003cspan\u003e from Books Harbour today and start building real models with the tools the industry actually uses.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eAbout This Hands-On Machine Learning Book\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe \u003c\/span\u003e\u003cspan\u003eHandson Machine Learning With Scikit Learn And Tensorflow\u003c\/span\u003e\u003cspan\u003e book is built on a simple principle: the best way to learn machine learning is to build things. \u003c\/span\u003e\u003cspan\u003eAurélien Géron\u003c\/span\u003e\u003cspan\u003e does not spend chapters on theory before letting you touch code. Each concept arrives with a working implementation so you can see exactly how it behaves in practice, not just on paper.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe 3rd Edition is the most current \u003c\/span\u003e\u003cspan\u003eAurelien Geron Book\u003c\/span\u003e\u003cspan\u003e, updated to cover \u003c\/span\u003e\u003cspan\u003eTensorFlow\u003c\/span\u003e\u003cspan\u003e 2, the modern \u003c\/span\u003e\u003cspan\u003eKeras\u003c\/span\u003e\u003cspan\u003e API, and recent developments including transformers and diffusion models. Published by O'Reilly Media in November 2022, it is comprehensive enough to serve as a complete machine learning guide from classical algorithms through state-of-the-art deep learning architectures. Among \u003c\/span\u003e\u003cspan\u003eScikit Learn Books\u003c\/span\u003e\u003cspan\u003e available today, this is the one that data scientists most consistently recommend to people who want to learn by doing.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWhat You Will Learn from Hands-On Machine Learning, 3rd Edition\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eEnd-to-end machine learning projects: data collection, preprocessing, training, evaluation, and deployment\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eScikit Learn Guide\u003c\/span\u003e\u003cspan\u003e for classical ML: linear regression, decision trees, random forests, SVMs, and ensemble methods\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eFeature engineering and data preparation techniques for real-world datasets\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eNeural network fundamentals: building and training feedforward networks with \u003c\/span\u003e\u003cspan\u003eKeras\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eScikit Learn Keras Tensorflow\u003c\/span\u003e\u003cspan\u003e integration: when to use each tool and how to combine them effectively\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eConvolutional neural networks for computer vision tasks\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eRecurrent neural networks, LSTMs, and GRUs for sequence and time-series data\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eTransformer architectures: attention mechanisms and their role in modern NLP models\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eGenerative models: autoencoders, GANs, and diffusion models\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eModel deployment: saving, loading, and serving models in production\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eReinforcement learning: policy gradients, Q-learning, and actor-critic methods\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eKey Features of Hands-On Machine Learning, 3rd Edition\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003ePrimary reference for \u003c\/span\u003e\u003cstrong\u003eHands On Machine Learning With Scikit Learn And Tensorflow\u003c\/strong\u003e\u003cspan\u003e in one volume\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003e3rd Edition (November 2022): updated for \u003c\/span\u003e\u003cstrong\u003eTensorFlow\u003c\/strong\u003e\u003cspan\u003e 2, the modern \u003c\/span\u003e\u003cstrong\u003eKeras\u003c\/strong\u003e\u003cspan\u003e\u003cstrong\u003e \u003c\/strong\u003eAPI, transformers, and diffusion models\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003e861 pages of practical, code-first machine learning content\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eWritten by \u003c\/span\u003e\u003cstrong\u003eAurélien Géron\u003c\/strong\u003e\u003cspan\u003e, a former Google engineer and recognized ML educator\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003ePublished by O'Reilly Media, the leading technical publisher for software and AI\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eCovers both classical ML with \u003c\/span\u003e\u003cstrong\u003eScikit-Learn\u003c\/strong\u003e\u003cspan\u003e and deep learning with \u003c\/span\u003e\u003cstrong\u003eKeras\u003c\/strong\u003e\u003cspan\u003e and \u003c\/span\u003e\u003cstrong\u003eTensorFlow\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eEnd-to-end projects that teach the full machine learning workflow\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWho Should Read This Hands-On Machine Learning Book?\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eSoftware engineers who want to move into machine learning or add ML capabilities to their existing work will find this the clearest path from programming skills to working models. The book assumes Python fluency but not prior machine learning experience, so you can start building real systems from the first chapter without needing a statistics background first.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eData scientists who have used individual \u003c\/span\u003e\u003cspan\u003eScikit Learn Books\u003c\/span\u003e\u003cspan\u003e or tutorials but want a comprehensive resource that covers both classical and deep learning in one place use this as their primary desk reference. Research engineers joining ML teams use the 3rd Edition to get up to speed on the modern \u003c\/span\u003e\u003cspan\u003eTensorFlow\u003c\/span\u003e\u003cspan\u003e and \u003c\/span\u003e\u003cspan\u003eKeras\u003c\/span\u003e\u003cspan\u003e ecosystem quickly. University students taking applied machine learning courses often find this book more useful than their assigned textbook because of how directly its projects connect to the work they will do in industry.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWhy Choose Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow?\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eMost machine learning books choose between theory and practice. This one does not make that choice. \u003c\/span\u003e\u003cspan\u003eAurélien Géron\u003c\/span\u003e\u003cspan\u003e builds enough conceptual understanding that you know why a method works, and then immediately shows you how to implement it. That combination is harder to achieve than it looks, and it is the main reason this \u003c\/span\u003e\u003cspan\u003eAurelien Geron Book\u003c\/span\u003e\u003cspan\u003e consistently tops the list of practitioner recommendations.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe 3rd Edition is also meaningfully better than the earlier editions, not just a version bump. The complete update to \u003c\/span\u003e\u003cspan\u003eTensorFlow\u003c\/span\u003e\u003cspan\u003e 2 and the modern \u003c\/span\u003e\u003cspan\u003eKeras\u003c\/span\u003e\u003cspan\u003e API means the code you write from this book is production-relevant. The new chapters on transformers and diffusion models mean the book covers architectures that are currently driving the field, not just the techniques of five years ago.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eFrequently Asked Questions\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: What is Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow about?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: It is a practical machine learning book by Aurélien Géron that teaches how to build intelligent systems using Scikit-Learn for classical ML and Keras and TensorFlow for deep learning. The 3rd Edition covers the latest tools and techniques through real coding examples and projects.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: What is the ISBN-13 for Hands-On Machine Learning, 3rd Edition?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: The ISBN-13 is 9781098125974 (ISBN-10: 1098125975). It is published by O'Reilly Media.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Is this the 3rd Edition?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: Yes. This is the 3rd Edition of Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, published by O'Reilly Media in November 2022. It is the most current version of this book.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Is this suitable for beginners in machine learning?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: It is best for readers who have basic Python programming skills. Prior machine learning experience is not required, as the book builds concepts from the ground up using practical examples. Complete beginners to Python will benefit from learning Python basics first.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Does this cover TensorFlow 2 and Keras?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: Yes. The 3rd Edition covers the modern TensorFlow 2 and Keras ecosystem, along with Scikit-Learn for classical machine learning. It also includes coverage of recent developments including diffusion models and transformers.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e \u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003eFor support, contact \u003ca href=\"mailto:support@mindabooks.com\"\u003esupport@mindabooks.com\u003c\/a\u003e.\u003c\/p\u003e","brand":"MindaBooks","offers":[{"title":"Default Title","offer_id":43199994069067,"sku":null,"price":47.01,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0687\/9769\/4027\/files\/handsonmachinelearning.jpg?v=1789982322"},{"product_id":"deep-learning-with-python-2nd-edition","title":"Deep Learning with Python, 2nd Edition (1617296864)","description":"\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eBuy Deep Learning with Python, 2nd Edition Online\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eDeep Learning with Python\u003c\/strong\u003e\u003cspan\u003e, 2nd Edition \u003cstrong\u003e(\u003c\/strong\u003e\u003c\/span\u003e\u003cstrong\u003eISBN-13 9781617296864\u003c\/strong\u003e\u003cspan\u003e\u003cstrong\u003e)\u003c\/strong\u003e is the \u003c\/span\u003e\u003cstrong\u003eDeep Learning With Python Book\u003c\/strong\u003e\u003cspan\u003e written by \u003c\/span\u003e\u003cstrong\u003eFrançois Chollet\u003c\/strong\u003e\u003cspan\u003e, the person who created \u003c\/span\u003e\u003cspan\u003eKeras\u003c\/span\u003e\u003cspan\u003e. That is not incidental: it means every explanation of how the Keras deep learning library works comes from the engineer who designed its API from the ground up, and that author-to-tool relationship gives this book a clarity and insight that no other deep learning with python resource can replicate.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003ePublished by Manning in December 2021, this 2nd Edition is updated for \u003c\/span\u003e\u003cspan\u003eTensorFlow\u003c\/span\u003e\u003cspan\u003e 2, the modern integrated \u003c\/span\u003e\u003cspan\u003eKeras\u003c\/span\u003e\u003cspan\u003e API, and the newer architectures such as transformers and diffusion models that have reshaped what is possible with deep learning since the 1st Edition appeared in 2017.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eAbout This Deep Learning with Python Book\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe \u003c\/span\u003e\u003cspan\u003edeep learning with python francois chollet\u003c\/span\u003e\u003cspan\u003e approach is code-first throughout. Rather than spending the first third of the book building mathematical foundations before you write a single line of code, this \u003c\/span\u003e\u003cspan\u003eDeep Learning With Python book\u003c\/span\u003e\u003cspan\u003e puts working models in your hands in the first chapter and then explains what they are doing and why. That sequence feels more like learning from a practitioner than studying from a textbook, which is why it works for people who have struggled with more theory-heavy resources.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe \u003c\/span\u003e\u003cspan\u003ekeras deep learning book\u003c\/span\u003e\u003cspan\u003e coverage is thorough enough to take you from a complete beginner at deep learning to someone who can build, train, and evaluate image classifiers, text models, and generative systems using the tools that production ML teams actually use. The \u003c\/span\u003e\u003cspan\u003efrancois chollet deep learning\u003c\/span\u003e\u003cspan\u003e framework of explanation gives you the intuition for each concept before the code, the code to build it, and the context to know when and why to use the approach in real projects.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWhat You Will Learn from Deep Learning with Python\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eCore neural network mechanics: how tensors, operations, and backpropagation work before you need to think about them explicitly\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eBuilding and training deep learning models with \u003c\/span\u003e\u003cspan\u003eKeras\u003c\/span\u003e\u003cspan\u003e and \u003c\/span\u003e\u003cspan\u003eTensorFlow\u003c\/span\u003e\u003cspan\u003e 2 from data preprocessing to evaluation\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eConvolutional neural networks for computer vision: image classification, feature extraction, and segmentation\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eSequence models: RNNs, LSTMs, and text processing for this \u003c\/span\u003e\u003cspan\u003eneural networks with python book\u003c\/span\u003e\u003cspan\u003e approach to NLP\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eTransformer architectures: how self-attention works and why transformers have replaced most sequence models\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eGenerative deep learning: variational autoencoders, GANs, and diffusion models for image generation\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eAdvanced \u003c\/span\u003e\u003cspan\u003eKeras\u003c\/span\u003e\u003cspan\u003e patterns: custom layers, custom training loops, and multi-input and multi-output models\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eBest practices: regularization, hyperparameter tuning, callbacks, and debugging failed training runs\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eModel deployment: saving, exporting, and serving trained models for production use\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eKey Features of Deep Learning with Python, 2nd Edition\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eThe definitive \u003c\/span\u003e\u003cstrong\u003eDeep Learning With Python Book\u003c\/strong\u003e\u003cspan\u003e by the creator of \u003c\/span\u003e\u003cspan\u003eKeras\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cstrong\u003eDeep Learning With Python 2nd Edition\u003c\/strong\u003e\u003cspan\u003e: fully updated for \u003c\/span\u003e\u003cspan\u003eTensorFlow\u003c\/span\u003e\u003cspan\u003e 2, transformers, and diffusion models\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003e504 pages, code-first from fundamentals through advanced generative models\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eThe most authoritative \u003c\/span\u003e\u003cstrong\u003ekeras deep learning book\u003c\/strong\u003e\u003cspan\u003e available, written by Keras creator \u003c\/span\u003e\u003cstrong\u003eFrançois Chollet\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003ePublished by Manning Publications, December 2021\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eCovers computer vision, NLP, transformers, and generative AI in one volume\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eFree standard US shipping on every order\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWho Should Read This Deep Learning with Python Book?\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003ePython developers who want to move into deep learning will find this the most direct path. If you already write Python and you want to start building neural networks without first becoming a linear algebra expert, this book gets you building real models fast. Software and web engineers who want to add python ai capabilities to their projects use it to learn enough deep learning to implement the features their work requires.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eData scientists and analysts who have used machine learning at the API level and want to understand what is happening inside their models will find this \u003c\/span\u003e\u003cspan\u003epython ai book\u003c\/span\u003e\u003cspan\u003e fills that gap without overwhelming them with theory. Anyone who has tried a \u003c\/span\u003e\u003cspan\u003emachine learning with python book\u003c\/span\u003e\u003cspan\u003e and found themselves stalled on math or theory will find Chollet's code-first style much more accessible.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eComputer science students who want applied deep learning experience alongside their coursework, and anyone preparing for a machine learning engineering role who needs hands-on \u003c\/span\u003e\u003cspan\u003eKeras\u003c\/span\u003e\u003cspan\u003e and \u003c\/span\u003e\u003cspan\u003eTensorFlow\u003c\/span\u003e\u003cspan\u003e 2 experience, will find this book gives them both the concepts and the practical skills their next employer expects.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWhy Choose Deep Learning with Python, 2nd Edition?\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThere are many deep learning books, but this is the only one where the author of the library and the author of the book are the same person. The \u003c\/span\u003e\u003cspan\u003edeep learning with python francois chollet\u003c\/span\u003e\u003cspan\u003e combination means that every design decision in \u003c\/span\u003e\u003cspan\u003eKeras\u003c\/span\u003e\u003cspan\u003e, every API choice, and every training pattern in this book is explained by someone who made those decisions. That insider knowledge produces a level of explanation that no other resource offers.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe 2nd Edition is also the version the field is at today. The 1st Edition from 2017 predates \u003c\/span\u003e\u003cspan\u003eTensorFlow\u003c\/span\u003e\u003cspan\u003e 2, the transformer revolution, and the emergence of diffusion models as a practical tool. If you are going to invest time in learning \u003c\/span\u003e\u003cspan\u003eDeep Learning With Python\u003c\/span\u003e\u003cspan\u003e, the 2nd Edition is the one to learn from because it teaches the tools and approaches currently used in production, not the tools of five years ago.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eFrequently Asked Questions\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: What is Deep Learning with Python, 2nd Edition about?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: It is a hands-on deep learning book by François Chollet, the creator of Keras, that teaches how to build neural networks and AI applications using Python, Keras, and TensorFlow 2. The 2nd Edition adds coverage of transformers, diffusion models, and modern best practices.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: What is the ISBN-13 for Deep Learning with Python, 2nd Edition?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: The ISBN-13 is 9781617296864 (ISBN-10: 1617296864). Published by Manning Publications, December 2021.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Is this suitable for beginners?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: It requires basic Python programming knowledge but no prior deep learning experience. It builds from fundamentals up. Readers who do not yet know Python should learn the language basics first before starting this book.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Why is François Chollet's authorship significant?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: Francois Chollet is the creator of Keras, the deep learning library this book teaches. That means the explanations of how Keras works come from the person who designed it, giving this book a depth of insight that no third-party author can replicate.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Does this cover TensorFlow?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: Yes. The 2nd Edition uses TensorFlow 2 and the integrated modern Keras API throughout. It also covers newer topics including transformers and diffusion models that were not in the 1st Edition.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e \u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003eFor support, contact \u003ca href=\"mailto:support@mindabooks.com\"\u003esupport@mindabooks.com\u003c\/a\u003e.\u003c\/p\u003e","brand":"MindaBooks","offers":[{"title":"Default Title","offer_id":43199995969611,"sku":null,"price":49.01,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0687\/9769\/4027\/files\/deep-learning-with-python-2nd-edition-cover_f0ffb99f-25b9-45cd-af30-ce7f6a8c14dc.jpg?v=1789982329"},{"product_id":"deep-learning-ian-goodfellow","title":"Deep Learning (Adaptive Computation and Machine Learning series) (0262035618)","description":"\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eBuy Deep Learning by Ian Goodfellow Online\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eDeep Learning\u003c\/strong\u003e\u003cspan\u003e (Adaptive Computation and \u003c\/span\u003e\u003cstrong\u003eMachine Learning\u003c\/strong\u003e\u003cspan\u003e series) \u003cstrong\u003e(\u003c\/strong\u003e\u003c\/span\u003e\u003cstrong\u003eISBN-13 9780262035613\u003c\/strong\u003e\u003cspan\u003e\u003cstrong\u003e)\u003c\/strong\u003e by \u003c\/span\u003e\u003cstrong\u003eIan Goodfellow\u003c\/strong\u003e\u003cspan\u003e, \u003c\/span\u003e\u003cstrong\u003eYoshua Bengio\u003c\/strong\u003e\u003cspan\u003e, and \u003c\/span\u003e\u003cstrong\u003eAaron Courville\u003c\/strong\u003e\u003cspan\u003e is the definitive \u003c\/span\u003e\u003cstrong\u003edeep learning textbook\u003c\/strong\u003e\u003cspan\u003e in academic and research settings worldwide. Published by \u003c\/span\u003e\u003cspan\u003eMIT Press\u003c\/span\u003e\u003cspan\u003e, it is the book that university courses, research labs, and machine learning teams assign when they want a single resource that covers both the theory and the practice of modern deep learning with real rigor.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eIf you are serious about understanding how \u003c\/span\u003e\u003cspan\u003edeep learning\u003c\/span\u003e\u003cspan\u003e actually works rather than just applying it as a black box, this is the book you need on your desk. At 45% off the original retail price, it is also the best time to add the \u003c\/span\u003e\u003cstrong\u003eDeep Learning Ian Goodfellow\u003c\/strong\u003e\u003cspan\u003e text to your technical library.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eAbout This Deep Learning Textbook\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe \u003c\/span\u003e\u003cspan\u003eDeep Learning Goodfellow\u003c\/span\u003e\u003cspan\u003e book is different from practitioner guides like Keras or PyTorch tutorials. It builds understanding from the mathematical ground up, starting with the linear algebra, probability theory, and numerical computation that underpin every deep learning system, and then moving through the core models, optimization methods, and modern architectures that researchers and engineers actually work with.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eAs the \u003c\/span\u003e\u003cspan\u003eMIT Deep Learning Book\u003c\/span\u003e\u003cspan\u003e in the Adaptive Computation and \u003c\/span\u003e\u003cspan\u003eMachine Learning\u003c\/span\u003e\u003cspan\u003e series, it carries the kind of academic authority that comes from authors who are themselves key contributors to the field. \u003c\/span\u003e\u003cspan\u003eIan Goodfellow\u003c\/span\u003e\u003cspan\u003e invented generative adversarial networks. \u003c\/span\u003e\u003cspan\u003eYoshua Bengio\u003c\/span\u003e\u003cspan\u003e and \u003c\/span\u003e\u003cspan\u003eAaron Courville\u003c\/span\u003e\u003cspan\u003e are Turing Award-level researchers whose work sits at the foundation of modern AI. When you read their explanations of how \u003c\/span\u003e\u003cspan\u003eneural networks\u003c\/span\u003e\u003cspan\u003e learn, you are reading from the people who developed many of those ideas.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWhat You Will Learn from Deep Learning by Ian Goodfellow\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eMathematical foundations for \u003c\/span\u003e\u003cspan\u003edeep learning\u003c\/span\u003e\u003cspan\u003e: linear algebra, probability, information theory, and numerical methods\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eHow \u003c\/span\u003e\u003cspan\u003emachine learning\u003c\/span\u003e\u003cspan\u003e algorithms learn from data: capacity, overfitting, underfitting, and generalization\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eDeep feedforward networks: how \u003c\/span\u003e\u003cspan\u003eneural networks\u003c\/span\u003e\u003cspan\u003e are structured and trained\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eRegularization techniques: dropout, batch normalization, data augmentation, and more\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eOptimization for \u003c\/span\u003e\u003cspan\u003edeep learning\u003c\/span\u003e\u003cspan\u003e: SGD, momentum, adaptive learning rates, and second-order methods\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eConvolutional \u003c\/span\u003e\u003cspan\u003eneural networks\u003c\/span\u003e\u003cspan\u003e: architecture, pooling, and applications in computer vision\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eRecurrent \u003c\/span\u003e\u003cspan\u003eneural networks\u003c\/span\u003e\u003cspan\u003e and LSTMs: sequence modeling and temporal dependencies\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003ePractical methodology: how to choose hyperparameters, debug models, and design experiments\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eDeep generative models: restricted Boltzmann machines, autoencoders, and generative adversarial networks\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eFrontier research areas: representation learning, structured probabilistic models, and Monte Carlo methods\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eKey Features of Deep Learning (Adaptive Computation and Machine Learning)\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eThe most authoritative \u003c\/span\u003e\u003cspan\u003eDeep Learning Textbook\u003c\/span\u003e\u003cspan\u003e available, used in graduate courses worldwide\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eWritten by \u003c\/span\u003e\u003cspan\u003eIan Goodfellow\u003c\/span\u003e\u003cspan\u003e, \u003c\/span\u003e\u003cspan\u003eYoshua Bengio\u003c\/span\u003e\u003cspan\u003e, and \u003c\/span\u003e\u003cspan\u003eAaron Courville\u003c\/span\u003e\u003cspan\u003e, three of the field's most cited researchers\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003ePart of the \u003c\/span\u003e\u003cspan\u003eMIT Press\u003c\/span\u003e\u003cspan\u003e \u003c\/span\u003e\u003cspan\u003eDeep Learning Adaptive Computation\u003c\/span\u003e\u003cspan\u003e and \u003c\/span\u003e\u003cspan\u003eMachine Learning\u003c\/span\u003e\u003cspan\u003e series\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003e800 pages covering both mathematical foundations and practical \u003c\/span\u003e\u003cspan\u003edeep learning\u003c\/span\u003e\u003cspan\u003e techniques\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eHardcover edition built for lasting reference use\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eCovers the full pipeline from data and optimization through advanced generative models\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003e•\u003c\/span\u003e\u003cspan\u003e     \u003c\/span\u003e\u003cspan\u003eFree standard US shipping on every order\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWho Should Read This Deep Learning Ian Goodfellow Book?\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eGraduate students in \u003c\/span\u003e\u003cspan\u003emachine learning\u003c\/span\u003e\u003cspan\u003e, computer science, and AI programs use this \u003c\/span\u003e\u003cspan\u003edeep learning textbook\u003c\/span\u003e\u003cspan\u003e as a core course reference. If your program covers \u003c\/span\u003e\u003cspan\u003eneural networks\u003c\/span\u003e\u003cspan\u003e at a theoretical level, this is almost certainly either required or recommended reading.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eResearchers entering the field use it to build the foundational understanding they need to read papers and contribute original work. Software engineers and data scientists who want to go beyond API calls and understand what their \u003c\/span\u003e\u003cspan\u003edeep learning\u003c\/span\u003e\u003cspan\u003e models are actually doing turn to this book when frameworks and tutorials stop being sufficient.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eLecturers and instructors teaching \u003c\/span\u003e\u003cspan\u003emachine learning\u003c\/span\u003e\u003cspan\u003e or \u003c\/span\u003e\u003cspan\u003edeep learning\u003c\/span\u003e\u003cspan\u003e courses keep it as a primary text because the coverage is broad enough for a full semester and rigorous enough to hold up to serious students. Anyone who finds themselves saying 'I want to actually understand this, not just use it' will find this book is the right starting point for that kind of understanding.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eWhy Choose Deep Learning Goodfellow Over Other AI Books?\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThere are many \u003c\/span\u003e\u003cspan\u003edeep learning\u003c\/span\u003e\u003cspan\u003e books, but most of them fall into one of two camps: they either teach you how to use a framework, or they skim the theory so lightly that you finish the book without really understanding why anything works. The \u003c\/span\u003e\u003cspan\u003eDeep Learning Goodfellow\u003c\/span\u003e\u003cspan\u003e text refuses to make that tradeoff. It covers the mathematics, the intuition, and the practical application, which is why it has remained the standard academic reference in the field since its publication in 2016.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eIt also ages better than framework-specific books. The mathematics of optimization, the principles behind regularization, the theory of convolutional networks, the intuition behind generative models: none of that has gone stale, because it is describing ideas rather than APIs. Engineers who read this book in 2016 still find it useful today, and engineers buying it now will find the same thing in five years.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cspan\u003eFrequently Asked Questions\u003c\/span\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: What is Deep Learning by Ian Goodfellow about?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: It is a comprehensive deep learning textbook that covers both the mathematical foundations and practical techniques behind modern neural networks and AI. Written by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, it is the most widely referenced academic resource in the field.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: What is the ISBN-13 for Deep Learning by Ian Goodfellow?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: The ISBN-13 is 9780262035613 (ISBN-10: 0262035618). It is published by MIT Press.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Is this the MIT Deep Learning Book?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: Yes. This is the deep learning book published by The MIT Press as part of the Adaptive Computation and Machine Learning series. It is commonly called the MIT deep learning book in academic and research communities.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Is this hardcover or paperback?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: This edition is a hardcover. It is 800 pages and published by MIT Press.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eQ: Is Deep Learning by Goodfellow suitable for beginners?\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eA: It is best suited for readers with a solid background in mathematics, including linear algebra, probability, and calculus, as well as some familiarity with programming. It is not an introductory text, but the early chapters do cover mathematical prerequisites for readers who need a refresher.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e \u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003eFor support, contact \u003ca href=\"mailto:support@mindabooks.com\"\u003esupport@mindabooks.com\u003c\/a\u003e.\u003c\/p\u003e","brand":"MindaBooks","offers":[{"title":"Default Title","offer_id":43199996559435,"sku":null,"price":54.65,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0687\/9769\/4027\/files\/DeepLearning_dd51a702-4c06-4394-8882-541242e1df6b.jpg?v=1789982331"}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0687\/9769\/4027\/collections\/Machine_Learning_AI_Books.webp?v=1789558527","url":"https:\/\/www.mindabooks.com\/collections\/machine-learning-ai.oembed","provider":"Minda Books","version":"1.0","type":"link"}