Hands-On Deep Learning with TensorFlow

This book is your guide to exploring the possibilities in the field of deep learning, making use of Google's TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data.
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Hands-On Deep Learning with TensorFlow

Dan Van Boxel

This book is your guide to exploring the possibilities in the field of deep learning, making use of Google's TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data.

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Book Details

ISBN 139781787282773
Paperback174 pages

Book Description

Dan Van Boxel’s Deep Learning with TensorFlow is based on Dan’s best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data.

With Dan’s guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data.

In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.

Table of Contents

Chapter 1: Getting Started
Installing TensorFlow
Simple computations
Logistic regression model building
Logistic regression training
Chapter 2: Deep Neural Networks
Basic neural networks
Single hidden layer model
Single hidden layer explained
The multiple hidden layer model
Results of the multiple hidden layer
Summary
Chapter 3: Convolutional Neural Networks
Convolutional layer motivation
Convolutional layer application
Pooling layer motivation
Pooling layer application
Deep CNN
Deeper CNN
Wrapping up deep CNN
Summary
Chapter 4: Introducing Recurrent Neural Networks
Exploring RNNs
TensorFlow learn
DNNs
Summary
Chapter 5: Wrapping Up
Research evaluation
A quick review of all the models
The future of TensorFlow
Summary

What You Will Learn

  • Set up your computing environment and install TensorFlow
  • Build simple TensorFlow graphs for everyday computations
  • Apply logistic regression for classification with TensorFlow
  • Design and train a multilayer neural network with TensorFlow
  • Intuitively understand convolutional neural networks for image recognition
  • Bootstrap a neural network from simple to more accurate models
  • See how to use TensorFlow with other types of networks
  • Program networks with SciKit-Flow, a high-level interface to TensorFlow

Authors

Table of Contents

Chapter 1: Getting Started
Installing TensorFlow
Simple computations
Logistic regression model building
Logistic regression training
Chapter 2: Deep Neural Networks
Basic neural networks
Single hidden layer model
Single hidden layer explained
The multiple hidden layer model
Results of the multiple hidden layer
Summary
Chapter 3: Convolutional Neural Networks
Convolutional layer motivation
Convolutional layer application
Pooling layer motivation
Pooling layer application
Deep CNN
Deeper CNN
Wrapping up deep CNN
Summary
Chapter 4: Introducing Recurrent Neural Networks
Exploring RNNs
TensorFlow learn
DNNs
Summary
Chapter 5: Wrapping Up
Research evaluation
A quick review of all the models
The future of TensorFlow
Summary

Book Details

ISBN 139781787282773
Paperback174 pages
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