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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems
£52.60£68.40 (-23%)
Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks–scikit-learn, Keras, and TensorFlow–to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurelien Geron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you’ve learned. Programming experience is all you need to get started. Use scikit-learn to track an example machine learning project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning Train neural nets using multiple GPUs and deploy them at scale using Google’s Vertex AI
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Additional information
| Publisher | O'Reilly Media, 3rd edition (31 Oct. 2022) |
|---|---|
| Language | English |
| Paperback | 850 pages |
| ISBN-10 | 1098125975 |
| ISBN-13 | 978-1098125974 |
| Dimensions | 18.42 x 5.08 x 24.13 cm |
by Amazon Customer Since 2001
This book is always next to my desk, an invaluable book for ML projects.Code examples that work, along with clear text that explains how and why.
by Jack
I’m just starting out through this book. It seems well written BUT the specific versions of Python libraries do not seem to be specified, and the APIs are unstable so the example Jupyter Notebooks do not work with the latest versions. Thats not good, I don’t want to be debugging complex demos when I’m just starting to learn the technology! For reference, you seem to need scikit-learn==1.1.3 and pandas==1.4.2. It would be trivial for the author to list this in the book.
by Amazon Customer
This is s asubstantial book – over 800 pages and so far there is no useless padding at all. Every page is a gem and full of useful information and insights. Within a week of starting the book, I built my first ML algo that successfully makes predictions on the out-of-sample test set, after many years of trying. So glad I chose this book above others, Would highly recommend for anyone wanting to achieve success with ML.
by Windy Dad
This book is very good but WHY do Amazon not package books appropriately and protect corners. Will be seeking a discount
by Windy Dad
Geron is an amazing author. The density of information is so high in this text it’s amazing. Anyone who takes their time and systematically works through this book carefully will make incredible progress in their understanding of and ability to implement machine learning algorithms. If I had to give someone one book to work through to become effective in implementing machine learning techniques in Python this would be it. To get more depth of understanding mathematically you could supplement your learning with Introduction to Statistical Learning by Hastie, Witten et al. Bravo Aurèlien, this is a masterpiece