Here is a curated list of books on deep learning that are highly recommended for both beginners and experts. These books cover a wide range of topics from the fundamentals of neural networks to advanced techniques and applications.

  • Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

    • This is the go-to book for anyone serious about understanding deep learning. It covers the mathematical foundations, algorithms, and practical applications of deep learning.
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  • Deep Learning with Python by François Chollet

    • Written by the creator of Keras, this book is a great resource for those who want to apply deep learning to real-world problems using Python. It's more practical and less mathematically rigorous than the Goodfellow et al. book.
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  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

    • This book provides a comprehensive introduction to machine learning with a focus on practical examples and real-world applications. It covers both traditional machine learning and deep learning.
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  • Deep Learning for Computer Vision with Python by Adrian Rosebrock

    • If you're interested in applying deep learning to computer vision tasks, this book is a great choice. It covers a range of topics from image classification to object detection.
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  • Deep Reinforcement Learning by Sergey Levine, Chelsea Finn, and Pieter Abbeel

    • This book delves into the intersection of deep learning and reinforcement learning, a field that is rapidly growing and has applications in areas like robotics and game playing.
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Deep Learning Book

For further reading on deep learning, don't miss our Deep Learning Tutorials.

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