HandsOn Machine Learning with ScikitLearn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
PDF
 eBook:HandsOn Machine Learning with ScikitLearn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
 Author:Aurélien Géron
 Edition:1 edition
 Categories:
 Data:April 9, 2017
 ISBN:1491962291
 ISBN13:9781491962299
 Language:English
 Pages:574 pages
 Format:PDF
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. This practical book shows you how.
By using concrete examples, minimal theory, and two productionready Python frameworks—scikitlearn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to 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.
 Explore the machine learning landscape, particularly neural nets
 Use scikitlearn to track an example machinelearning project endtoend
 Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
 Use the TensorFlow library to build and train neural nets
 Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
 Learn techniques for training and scaling deep neural nets
 Apply practical code examples without acquiring excessive machine learning theory or algorithm details

Content
1. The Machine Learning Landscape
2. EndtoEnd Machine Learning Project
3. Classification
4. Training Models
5. Support Vector Machines
6. Decision Trees
7. Ensemble Learning and Random Forests
8. Dimensionality Reduction
Part II. Neural Networks and Deep Learning
9. Up and Running with TensorFlow
10. Introduction to Artificial Neural Networks
11. Training Deep Neural Nets
12. Distributing TensorFlow Across Devices and Servers
13. Convolutional Neural Networks
14. Recurrent Neural Networks
15. Autoencoders
16. Reinforcement Learning
Free sample
