TensorFlow Machine Learning Cookbook - Second Edition

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TensorFlow Machine Learning Cookbook - Second Edition

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內容描述

Skip the theory and get the most out of Tensorflow to build production-ready machine learning models
Key Features

Exploit the features of Tensorflow to build and deploy machine learning models
Train neural networks to tackle real-world problems in Computer Vision and NLP
Handy techniques to write production-ready code for your Tensorflow models

Book Description
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before.
With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production.
By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.
What you will learn

Become familiar with the basic features of the TensorFlow library
Get to know Linear Regression techniques with TensorFlow
Learn SVMs with hands-on recipes
Implement neural networks to improve predictive modeling
Apply NLP and sentiment analysis to your data
Master CNN and RNN through practical recipes
Implement the gradient boosted random forest to predict housing prices
Take TensorFlow into production

Who this book is for
If you are a data scientist or a machine learning engineer with some knowledge of linear algebra, statistics, and machine learning, this book is for you. If you want to skip the theory and build production-ready machine learning models using Tensorflow without reading pages and pages of material, this book is for you. Some background in Python programming is assumed.
Table of Contents

Getting Started with TensorFlow
The TensorFlow Way
Linear Regression
Support Vector Machines
Nearest Neighbor Methods
Neural Networks
Natural Language Processing
Convolutional Neural Networks
Recurrent Neural Networks
Taking TensorFlow to Production
More with TensorFlow