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Hands-On Convolutional Neural Networks with TensorFlow

By : Araujo, Zafar, Tzanidou, Burton, Patel

4 (3)

Buy this Book

Hands-On Convolutional Neural Networks with TensorFlow

4 (3)

By: Araujo, Zafar, Tzanidou, Burton, Patel

Buy this Book

Overview of this book

Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Setup and Introduction to TensorFlow

The TensorFlow way of thinking

Setting up and installing TensorFlow

TensorFlow API levels

Building your first TensorFlow model

Creating TensorFlow graphs

Feeding data with placeholders

Initializing variables

Training our model

Evaluating a trained model

The session

Summary

Deep Learning and Convolutional Neural Networks

AI and ML

Artificial neural networks

Convolutional neural networks

Building a CNN model in TensorFlow

Summary

Image Classification in TensorFlow

CNN model architecture

Datasets

Image classification with TensorFlow

Model Initialization

Improving generalization by regularizing

Summary

Object Detection and Segmentation

Image classification with localization

Object detection as classification – Sliding window

Single Shot Detectors – You Only Look Once

Semantic segmentation

Instance segmentation

Summary

VGG, Inception Modules, Residuals, and MobileNets

Substituting big convolutions

VGGNet

GoogLeNet

Residual Networks

MobileNets

Summary

Autoencoders, Variational Autoencoders, and Generative Adversarial Networks

Why generative models

Autoencoders

Variational autoencoders

Generative adversarial networks

Summary

Transfer Learning

When?

How? An overview

How? Code example

Summary

Machine Learning Best Practices and Troubleshooting

Building Machine Learning Systems

Data Preparation

Bias and Variance

Data Imbalance

Evaluation Metrics

Code Structure best Practice

Recipe for CNN creation

Summary

Training at Scale

Storing data in TFRecords

Making efficient pipelines

Distributed computing in TensorFlow

When data does not fit on one computer

Scaling computation in the cloud

Summary

References

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 7

Chapter 9

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Model Initialization

As we add more and more layers to our models, it becomes harder and harder to train them using backpropagation. The error values that are passed back through the model to update weights become smaller and smaller the deeper we go. This is known as the vanishing gradient problem.

As a result, an important thing to look at before we start training our models is what values we initialize our weights to. A bad initialization can make the model very slow to converge, or perhaps never converge at all.

Although we do not know exactly what values our weights will end up with after training, one might reasonably expect that about half of them will be positive values and half will be negative.