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TensorFlow: Powerful Predictive Analytics with TensorFlow电子书

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10人正在读 | 0人评论 9.8

作       者:Md. Rezaul Karim

出  版  社:Packt Publishing

出版时间:2018-03-14

字       数:101.5万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Learn how to solve real life problems using different methods like logic regression, random forests and SVM’s with TensorFlow. About This Book ? Understand predictive analytics along with its challenges and best practices ? Embedded with assessments that will help you revise the concepts you have learned in this book Who This Book Is For This book is aimed at developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. What You Will Learn ? Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration ? Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics ? Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics ? Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations ? Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasets In Detail Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google’s brainchild, is immensely popular and extensively used for predictive analysis. This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. Style and approach This is a fast-paced guide that provides a quick learning solution to all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow Note: This book is a blend of text and quizzes, all packaged up keeping your journey in mind. It includes content from the following Packt product: ? Predictive Analytics with TensorFlow by Md. Rezaul Karim
目录展开

TensorFlow: Powerful Predictive Analytics with TensorFlow

Credits

Meet Your Expert

Preface

What's in It for Me?

What Will I Get From This Book?

Prerequisites

Chapter 1. From Data to Decisions – Getting Started with TensorFlow

Taking Decisions Based on Data – Titanic Example

Data Value Chain for Making Decisions

From Disaster to Decision – Titanic Survival Example

General Overview of TensorFlow

Installing and Configuring TensorFlow

Note

Installing TensorFlow on Linux

Installing Python and nVidia Driver

Note

Installing NVIDIA CUDA

Installing NVIDIA cuDNN v5.1+

Installing the libcupti-dev Library

Installing TensorFlow

Note

Installing TensorFlow with native pip

Installing with virtualenv

Installing TensorFlow from Source

Note

Testing Your TensorFlow Installation

TensorFlow Computational Graph

TensorFlow Programming Model

Note

Note

Note

Data Model in TensorFlow

Tensors

Note

Rank

Shape

Data Type

Note

Note

Variables

Fetches

Feeds and Placeholders

Note

TensorBoard

How Does TensorBoard Work?

Getting Started with TensorFlow – Linear Regression and Beyond

Source Code for the Linear Regression

Summary

Assessments

Chapter 2. Putting Data in Place – Supervised Learning for Predictive Analytics

Supervised Learning for Predictive Analytics

Linear Regression – Revisited

Problem Statement

Using Linear Regression for Movie Rating Prediction

Note

Note

From Disaster to Decision – Titanic Example Revisited

An Exploratory Analysis of the Titanic Dataset

Feature Engineering

Logistic Regression for Survival Prediction

Using TensorFlow Contrib

Note

Linear SVM for Survival Prediction

Note

Note

Ensemble Method for Survival Prediction – Random Forest

Note

A Comparative Analysis

Summary

Assessments

Chapter 3. Clustering Your Data – Unsupervised Learning for Predictive Analytics

Unsupervised Learning and Clustering

Using K-means for Predictive Analytics

How K-means Works

Note

Using K-means for Predicting Neighborhoods

Note

Predictive Models for Clustering Audio Files

Using kNN for Predictive Analytics

Working Principles of kNN

Implementing a kNN-Based Predictive Model

Note

Summary

Assessments

Chapter 4. Using Reinforcement Learning for Predictive Analytics

Reinforcement Learning

Reinforcement Learning in Predictive Analytics

Notation, Policy, and Utility in RL

Policy

Utility

Developing a Multiarmed Bandit's Predictive Model

Developing a Stock Price Predictive Model

Summary

Assessments

Appendix A. Assessment Answers

Lesson 1: From Data to Decisions – Getting Started with TensorFlow

Lesson 2: Putting Data in Place – Supervised Learning for Predictive Analytics

Lesson 3: Clustering Your Data – Unsupervised Learning for Predictive Analytics

Lesson 4: Using Reinforcement Learning for Predictive Analytics

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