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Regression

This chapter shows how regression can be done using TensorFlow. In this chapter, we will cover the following topics:

Choosing loss functions
Optimizers in TensorFlow
Reading from CSV files and preprocessing data
House price estimation-simple linear regression
House price estimation-multiple linear regression
Logistic regression on the MNIST dataset

Key benefits

Skill up and implement tricky neural networks using Google's TensorFlow 1.x

An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more.

Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment

Description

Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve real-life problems in the artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google’s open source framework for deep learning. You will implement different deep learning networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs), with easy-to-follow standalone recipes. You will learn how to use TensorFlow with Keras as the backend. You will learn how different DNNs perform on some popularly used datasets, such as MNIST, CIFAR-10, and Youtube8m. You will not only learn about the different mobile and embedded platforms supported by TensorFlow, but also how to set up cloud platforms for deep learning applications. You will also get a sneak peek at TPU architecture and how it will affect the future of DNNs. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, and autoencoders.

Who is this book for?

This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful.

What you will learn

• Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code

• Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box

• Use different regression techniques for prediction and classifi cation problems

• Build single and multilayer perceptrons in TensorFlow

• Implement a CNN and a RNN in TensorFlow, and use them to solve real-world problems

• Learn how Restricted Boltzmann Machines can be used to recommend movies

• Understand the implementation of autoencoders and deep belief networks, and use them for emotion detection

• Master the different reinforcement learning methods in order to implement game playing agents

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Neural Network Programming with TensorFlow

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NSingh Jan 15, 2018

Without using complicated maths, authors have very meticulously implemented deep neural networks.The book is not for novices; instead, it is for advanced users. You will make most of the book if you:1. Are advanced Python users and looking to build an understanding of Deep Neural Networks. OR2. Know deep learning, and want to shift to TensorFlow for your work.The recipes use standard datasets already built into TensorFlow library, and this allows one to focus on implementation details of the model.I especially liked the chapter on Autoencoders as it very exhaustively covered the implementation of different types of Autoencoders on the same dataset allowing me to compare their performance. The chapter on Reinforcement learning was refreshingly simple, I could copy the recipe and with little modification had my policy gradient model working in the different game environment in no time.The book also gives step by step instruction on how to use Tensorflow models for mobile apps both on IOS and Android.The USP of the book is that it covers almost all DL models starting from the basic MLP to the recently released CAPSNet. Another great thing about the book is that it has given the references to the research papers it has implemented. The hands-on approach of the book makes it a useful reference for research.For codes, I would suggest you use the GitHub link provided in the book.Overall it is a great book.

Amazon Verified review

Juan Lopez Feb 05, 2018

Even for a good chef a recipe book always come handy, and thanks to the authors all Deep Learning professionals, enthusiasts, or beginners will have this Cookbook for consulting. From simple Linear Regressions to Generative Adversarial Networks you will find easy recipes to follow and learn how to implement all these kind of networks.

Gayan Aug 18, 2018

Good product good read

Kishore A Feb 16, 2018

This book gives a very good overview of the various algorithms to get one started in deep learning. I have especially liked the fact that it walks the reader through multiple use cases across all the algorithms. Additionally, it has a broad coverage including CNN, RNN, RL, Auto encoders, Capsule networks and even the implementation of tensorflow on mobile devices.Definitely a great buy!

A. Jaokar Jan 11, 2018

I bought this book (physical copy) from the Packt based on reading the author's previous book(on Keras). I have enjoyed the previous book and use it extensively because of its practical implementation approach. The same is followed here (but for core tensorflow instead of Keras). The cookbook approach helps a lot for tensorflow. Apart from CNNs, RNNS etc the book also covers Tensorflow mobile (which is of interest for me personally). The section on distributed tensorflow(Chapter 12) was new and interesting for me. Like in the first book, I referred to the author's git repo which I found useful. I used this book mainly to get a deeper understanding of tensorflow(I was starting from keras). It is also a good approach to learning tensorflow itself because of the cookbook approach. It even talks of Capsule networks. Hence, at the time of review(Jan 18) - its as current as Tensorflow can be. The distributed tensorflow also covers Azure and Amazon in addition to Google cloud(was nice to see the open approach). finally, I suggest that future versions of the book could expand on TPUs(also an interest for me based on my work)

TensorFlow 1.x Deep Learning Cookbook: Over 90 unique recipes to solve artificial-intelligence driven problems with Python

What do you get with Print?

TensorFlow 1.x Deep Learning Cookbook

Regression

Introduction

Choosing loss functions

Getting ready

Optimizers in TensorFlow

Getting ready

Reading from CSV files and preprocessing data

Getting ready

House price estimation-simple linear regression

Getting ready

How to do it...

House price estimation-multiple linear regression

How to do it...

Logistic regression on the MNIST dataset

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