کتاب Python Deep Learning , 2nd Edition

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https://www.amazon.co.uk/Python-Deep-Learning-techniques-architectures/dp/1789348463
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Python Deep Learning , 2nd Edition

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition

by Ivan Vasilev (Author), Daniel Slater (Author), Gianmario Spacagna (Author), Peter Roelants (Author), & ۱ more
Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries

Key Features
Build a strong foundation in neural networks and deep learning with Python libraries
Explore advanced deep learning techniques and their applications across computer vision and NLP
Learn how a computer can navigate in complex environments with reinforcement learning
Book Description
With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects.

This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You’ll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota.

By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.