Hands-On Machine Learning with Scikit-Learn,Keras,and TensorFlow: Concepts,Tools,and Techniques to Build Intelligent Systems
Authors: Aurélien Géron
ISBN-10: 1492032646
ISBN-13: 9781492032649
Edition 版次: 2
Publication Date 出版日期: 2019-10-15
Print Length 页数: 856 pages
Book Description
By finelybook
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
Preface
1.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
.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.S/M Dual Problem
D.Autodiff
E.Other Popular ANN Architectures
F.Special Data Structures
G.TensorFlow Graphs
Index
