Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems
Aurélien Géron (Author)
Through a series of recent 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. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow 2-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. NEW FOR THE SECOND EDITION:Updated all code to TensorFlow 2Introduced the high-level Keras APINew and expanded coverage including TensorFlow's Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more With Early Release ebooks, you get books in their earliest form-the author's raw and unedited content as he or she writes-so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released
Instructional and educational works
1 online resource (xxvii, 821 pages) : illustrations
9781492032618, 9781492032595, 1492032611, 149203259X
1099548239
Preface
Part I. The fundamentals of machine learning. 1. The Machine learning landscape
2. End-to-end machine learning project
3. Classification
4. Training models
5. Support vector machines
6. Decision trees
7. Ensemble learning and random forests
8. Dimensionality reduction
9. Unsupervised learning techniques
Part II. Neural networks and deep learning. 10. Introduction to artificial neural networks with Keras
11. Training deep neural networks
12. Custom models and training with TensorFlow
13. Loading and preprocessing data with TensorFlow
14. Deep computer vision using convolutional neural networks
15. Processing sequences using RNNs and CNNs
16. Natural language processing with RNNs and Attention
17. Representation learning and generative learning using autoencoders and GANs
18. Reinforcement learning
19. Training and deploying TensorFlow models at scale
A. Exercise solutions
B. Machine learning project checklist
C. SVM dual problem
D. Autodiff
E. Other popular ANN architectures
F. Special data structures
G. TensorFlow graphs
Index
"2nd edition updated for TensorFlow 2"--Cover