PYTHON MACHINE LEARNING: MACHINE LEARNING AND DEEP LEARNING FROM SCRATCH ILLUSTRATED WITH PYTHON, SCIKIT-LEARN, KERAS, THEANO AND TENSORFLOW

Book Description

Have you always wanted to learn deep learning but are afraid it’ll be too difficult for you?

This book is for you.
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
Book Description

Python Machine Learning, is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you’ll keep coming back to as you build your machine learning systems.
Packed with clear explanations, visualizations, and working examples, the book covers most of the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, this tutorial book teaches the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow, skit-learn, Keras, and theano , this edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It’s also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores analysis by giving some examples, helping you learn how to use machine learning algorithms to classify or predict documents output.
This book is your companion to machine learning with Python, whether you’re a Python developer new to machine learning or want to deepen your knowledge of the latest developments.
What you will learn
•Master the frameworks, models, and techniques that enable machines to ‘learn’ from data
•Use scikit-learn for machine learning and TensorFlow for deep learning
•Apply machine learning to classification, predict predict customer churning , and more
•Build and train neural networks, GANs, CNN, and other models
•Discover best practices for evaluating and tuning models
•Predict target outcomes using optimization algorithm such as Gradient Descent algorithm analysis
•Overcome challenges in deep learning algorithms by using dropout, regulation

•Who This Book Is For
If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.

Table of Contents

1.Giving Computers the Ability to Learn from Data
2.Training Simple ML Algorithms for Classification
3.ML Classifiers Using scikit-learn
4.Building Good Training Datasets – Data Preprocessing
5.Compressing Data via Dimensionality Reduction
6.Best Practices for Model Evaluation and Hyperparameter Tuning
7.Combining Different Models for Ensemble Learning
8.Predicting Continuous Target Variables with supversized learning
9.Implementing Multilayer Artificial Neural Networks
10.Modeling Sequential Data Using Recurrent Neural Networks
11.GANs for Synthesizing New Data

…and so much more….
In every chapter, you can edit the examples online

中文：

书名：PYTHON机器学习：机器学习和深度学习从头开始–以PYTHON、SCIKIT-LEARN、KERAS、Theano和TensorFlow为例

你是不是一直想学深度学习，但又怕对你来说太难了？

这本书是给你的。
深度学习是机器学习的一种形式，它使计算机能够从经验中学习，并根据概念的层次结构理解世界。因为计算机从经验中收集知识，所以计算机操作员不需要正式地指定计算机需要的所有知识。概念的层次结构允许计算机通过从简单的概念中构建复杂的概念来学习复杂的概念；这些层次结构的图表将有很多层深。这本书介绍了深度学习的广泛主题。
图书描述

是关于机器学习和深度学习的全面指南。它既是一个循序渐进的教程，也是你在构建机器学习系统时会经常参考的参考资料。
这本书充满了清晰的解释、可视化和工作示例，深入涵盖了大多数基本的机器学习技术。虽然有些书只教你遵循指令，但在这本机器学习书中，这本教程书教你机器学习背后的原理，允许你为自己构建模型和应用程序。更新为TensorFlow，短剧-学习，Kera，和Theano，这个版本向读者介绍其新的Kera API功能，以及最新增加的SCRICKIT-学习。它还扩展到涵盖基于深度学习的尖端强化学习技术，以及对Gans的介绍。最后，本书还通过给出一些例子来探索分析，帮助您学习如何使用机器学习算法来分类或预测文档输出。
无论您是机器学习的新手，还是想加深对最新发展的了解，这本书都是您与机器学习结合在一起的伴侣。
你将学到什么
·掌握使机器能够从数据中学习的框架、模型和技术
•Use scikit-learn for machine learning and TensorFlow for deep learning
·将机器学习应用于分类、预测客户流失等
•Build and train neural networks, GANs, CNN, and other models
·了解评估和调整模型的最佳实践
·使用优化算法(如梯度下降算法分析)预测目标结果
·通过使用辍学、调节来克服深度学习算法中的挑战

·本书的读者是谁？
如果你知道一些Python，并且你想使用机器学习和深度学习，那就拿起这本书。无论你是想从头开始，还是想扩展你的机器学习知识，这都是一个必不可少的资源。这本书是为想要创建实用的机器学习和深度学习代码的开发人员和数据科学家编写的，对于任何想要教计算机如何从数据中学习的人来说都是理想的。

目录表

1.赋予计算机从数据中学习的能力
2.训练简单的最大似然分类算法
3.使用SCRICKIT的ML分类器-学习
4.构建良好的训练数据集&数据预处理
5.通过降维压缩数据
6.模型评估和超参数调整的最佳实践
7.Combining Different Models for Ensemble Learning
8.Predicting Continuous Target Variables with supversized learning
9.实现多层人工神经网络
10.利用递归神经网络对序列数据进行建模
11.GANs for Synthesizing New Data

还有更多的东西。
在每一章中，您都可以在线编辑示例