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Python, scikit-learn, and TensorFlow 2, 3rd Edition

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Read more What's new in this third edition?? Many readers have told us how much they love the
first 12 chapters of the book as a comprehensive introduction to machine learning and Python's
scientific computing stack. To keep these chapters relevant and to improve the explanations
based on reader feedback, we updated them to support the latest versions of NumPy, SciPy, and
scikit-learn. One of the most exciting events in the deep learning world was the release of
TensorFlow 2. Consequently, all the TensorFlow-related deep learning chapters have received a
big overhaul. Since TensorFlow 2 introduced many new features and fundamental changes, we
rewrote these chapters from scratch. Furthermore, we added a new chapter on Generative
Adversarial Networks, which are one of the hottest topics in deep learning research, as well as a
comprehensive introduction to reinforcement learning based on numerous requests from readers.
Read more What are the key takeaways from your book? Machine learning can be useful in
almost every problem domain. We cover a lot of different subfields of machine learning in the
book. My hope is that people can find inspiration for applying these fundamental techniques to
drive their research or industrial applications. Also, using well-developed and maintained open
source software makes machine learning very accessible to a wide audience of experienced
programmers, as well as those who are new to programming. Python Machine Learning Third
Edition is also different from a classic academic machine learning textbook due to its emphasis on
practical code examples. However, I think this approach is highly valuable for both students and
young researchers who are getting started in machine learning and deep learning. We heard from
readers of previous editions that the book strikes a good balance between explaining the broader
concepts supported with great hands-on examples, giving a light introduction to the mathematical
underpinnings. Read more Why is it important to learn about GANs and reinforcement learning??
The first GANs paper had just come out two years before we started working on the second
edition, but we weren't sure of its relevance. However, GANs have evolved into one of the hottest
and most widely used deep learning techniques. People use them for creating artwork, colorizing
and improving the quality of photos, and to recreate old video game textures in higher resolutions.
It goes without saying that an introduction to GANs was long overdue. Another important machine
learning topic not included in previous editions is reinforcement learning, which has received a
massive boost in attention recently. Thanks to impressive projects such as DeepMind's AlphaGo
and AlphaGo Zero, reinforcement learning has received extensive news coverage. And just
recently, it’s been used to compete with the world's top e-sports players in the real-time strategy
video game StarCraft II. We hope that our new chapters can provide an accessible and practical
introduction to this exciting field. Read more Python Machine Learning, 3rd Edition Machine
Learning with PyTorch and Scikit-Learn Technology Used TensorFlow, scikit-learn PyTorch, scikitlearn
Reader Knowledge Level Beginner to intermediate Beginner to intermediate New Topics
Revised and expanded to include GANs, and reinforcement learning New content on transformers,
gradient boosting, and graph neural networks