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. 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. 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. Python Machine Learning, 3rd Edition Machine Learning with PyTorch and Scikit-Learn Technology Used TensorFlow, scikit-learn PyTorch, scikit-learn 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
✔ Author(s): Sebastian Raschka,Vahid Mirjalili
✔ Title: Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
✔ Rating : 4.5 out of 5 base on (428 reviews)
✔ ISBN-10: 1789955750
✔ Language: English
✔ Format ebook: PDF, EPUB, Kindle, Audio, HTML and MOBI
✔ Device compatibles: Android, iOS, PC and Amazon Kindle
Readers' opinions about Python Machine Learning by Sebastian Raschka
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