Author: Aurelien Geron
Pub Date: 2017
ISBN: 978-1491962299
Pages: 581
Language: English
Format: EPUB/AZW3/PDF (conv)
Size: 56 Mb

Concepts, Tools, and Techniques to Build Intelligent Systems
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 production-ready Python frameworks—scikit-learn 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 scikit-learn to track an example machine-learning project end-to-end
• 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
  

Table of Contents
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
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
A. Exercise Solutions
B. Machine Learning Project Checklist
C. SVM Dual Problem
D. Autodiff
E. Other Popular ANN Architectures