At MentorCruise, we are all about making the most out of the experience of others. As part of that, we've connected and asked dozens of experts and professionals about their favourite Deep Learning books – and here are the answers.
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Understanding the concepts of Deep Learning starts with understanding the fundamentals. On your way to mastery, it's crucial for you to understand how certain concepts were derived, and why things work like they do. Starting with these resources is the best way to do so.
A generative model is a type of machine learning model that aims to learn the underlying patterns or distributions of data in order to generate new, similar data. In essence, it's like teaching a computer to dream up its own data based on what it has seen before.
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At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests.
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Artificial Intelligence: A Guide for Thinking Humans provides readers with an accessible, entertaining, and clear-eyed view of the AI landscape, what the field has actually accomplished, how much further it has to go, and what it means for all of our futures.
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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”
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Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.
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This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. Youâ?? ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.
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These books are not required for you to learn Deep Learning, but they are highly recommended for you to deepen your knowledge.
Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks.
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“Deep Learning with PyTorch” by Eli Stevens, Luca Antiga, Thomas Viehmann: This book provides a hands-on approach to learning deep learning and PyTorch. It covers the basics of deep learning and PyTorch, and provides hands-on examples of implementing various deep learning models using PyTorch.
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The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications.
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Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline's techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn.
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Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation.
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This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learni…
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