Initially, this project started as the 4th edition of Python Machine Learning. However, after putting so much passion and hard work into the changes and new topics, we thought it deserved a new title. So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing what this book covers in general, I’d describe it as a comprehensive resource on the fundamental concepts of machine learning and deep learning. The first half of the book introduces readers to machine learning using scikit-learn, the defacto approach for working with tabular datasets. Then, the second half of this book focuses on deep learning, including applications to natural language processing and computer vision.

Features

  • Basic knowledge of Python is required
  • This book will take readers on a journey from understanding machine learning from the ground up
  • Train advanced deep learning models by the end of the book
  • We added a new chapter on Generative Adversarial Networks
  • Comprehensive introduction to reinforcement learning based on numerous requests from readers
  • book’s GitHub repository with code examples, table of contents, and additional information

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License

MIT License

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