Python Deep Learning
by Gianmario Spacagna and Daniel Slater
Print Length 页数: 406 pages
Publisher finelybook 出版社: Packt Publishing (28 April 2017)
Language 语言: English
ISBN-10: 1786464454
ISBN-13: 9781786464453
B071RCDRKD
Key Features
Explore and create intelligent systems using cutting-edge deep learning techniques
Implement deep learning algorithms and work with revolutionary libraries in Python
Get real-world examples and easy-to-follow tutorials on Theano,TensorFlow,H2O and more
Book Description
By finelybook
With an increasing interest in AI around the world,deep learning has attracted a great deal of public attention. Every day,deep learning algorithms are used broadly across different industries.
The book will give you all the practical information available on the subject,including the best practices,using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results.
Starting with a quick recap of important machine learning concepts,the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead,you will learn to use the latest open source libraries such as Theano,Keras,Google’s TensorFlow,and H20. Use this guide to uncover the difficulties of pattern recognition,scaling data with greater accuracy and discussing deep learning algorithms and techniques.
Whether you want to dive deeper into Deep Learning,or want to investigate how to get more out of this powerful technology,you’ll find everything inside.
What you will learn
Get a practical deep dive into deep learning algorithms
Explore deep learning further with Theano,Caffe,Kera,and TensorFlow
Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines
Dive into Deep Belief Nets and Deep Neural Networks
Discover more deep learning algorithms with Dropout and Convolutional Neural Networks
Get to know device strategies so you can use deep learning algorithms and libraries in the real world
Contents
Chapter 1. Machine Learning – An Introduction
Chapter 2. Neural Networks
Chapter 3. Deep Learning Fundamentals
Chapter 4. Unsupervised Feature Learning
Chapter 5. Image Recognition
Chapter 6. Recurrent Neural Networks and Language Models
Chapter 7. Deep Learning for Board Games
Chapter 8. Deep Learning for Computer Games
Chapter 9. Anomaly Detection
Chapter 10. Building a Production-Ready Intrusion Detection System
主要特征
使用尖端的深度学习技术探索和创建智能系统
实现深度学习算法，并与Python中的革命库一起工作
在Theano，TensorFlow，H2O等上获得真实的例子和易于遵循的教程
图书说明
随着对全球AI的关注越来越多，深刻的学习引起了广泛的关注。每天，不同行业广泛使用深度学习算法。
本书将为您提供有关该主题的所有实用信息，包括使用真实用例的最佳做法。您将学习识别和提取信息，以提高预测精度并优化结果。
从重要的机器学习概念的简要回顾开始，本书将深入研究使用Sci-kit学习的深入学习原理。继续前进，您将学习使用最新的开源库，如Theano，Keras，Google的TensorFlow和H20。使用本指南来发现模式识别的困难，更准确地缩放数据，并讨论深度学习算法和技术。
无论您想深入深入学习，还是想调查如何从这种强大的技术中获得更多信息，您会发现内部的一切。
你会学到什么
深入学习深入学习算法
与Theano，Caffe，Kera和TensorFlow进一步深入学习
了解两种最强大的技术，这些技术是许多实用的深入学习实现的核心: 自动编码器和限制玻尔兹曼机器
潜入深层信念网和深层神经网络
用Dropout和卷积神经网络发现更深入的学习算法
了解设备策略，以便您可以在现实世界中使用深度学习算法和库
目录
第一章机器学习 – 介绍
第二章神经网络
第三章深度学习基础知识
第四章无监督的特征学习
第五章图像识别
第六章经常性神经网络和语言模型
第七章棋盘游戏深度学习
第八章电脑游戏深度学习
第九章异常检测
第10章构建生产就绪入侵检测系统
