Python-Machine-Learning-Machine-Learning-and-Deep-Learning-with-Python-scikitlearn-and-TensorFlow-2-3rd-Edition

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Python Machine Learning: Machine Learning and Deep
Learning with Python, scikit-learn, and TensorFlow 2, 3rd
Edition
Sinopsis :
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, 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