Practical Convolutional Neural Networks

By : Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari

Practical Convolutional Neural Networks

By: Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari

Overview of this book

Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Deep Neural Networks – Overview

Building blocks of a neural network

Introduction to TensorFlow

Introduction to the MNIST dataset

Keras deep learning library overview

Handwritten number recognition with Keras and MNIST

Understanding backpropagation

Summary

Introduction to Convolutional Neural Networks

History of CNNs

Convolutional neural networks

Practical example – image classification

Summary

Build Your First CNN and Performance Optimization

CNN architectures and drawbacks of DNNs

Convolution and pooling operations in TensorFlow

Training a CNN

Building, training, and evaluating our first CNN

Model performance optimization

Summary

Popular CNN Model Architectures

Introduction to ImageNet

LeNet

AlexNet architecture

VGGNet architecture

GoogLeNet architecture

ResNet architecture

Summary

Transfer Learning

Feature extraction approach

Transfer learning example

Multi-task learning

Summary

Autoencoders for CNN

Introducing to autoencoders

Convolutional autoencoder

Applications

Summary

Object Detection and Instance Segmentation with CNN

The differences between object detection and image classification

Traditional, nonCNN approaches to object detection

R-CNN – Regions with CNN features

Fast R-CNN – fast region-based CNN

Faster R-CNN – faster region proposal network-based CNN

Mask R-CNN – Instance segmentation with CNN

Instance segmentation in code

References

Summary

GAN: Generating New Images with CNN

Pix2pix - Image-to-Image translation GAN

GAN – code example

Feature matching

Summary

Attention Mechanism for CNN and Visual Models

Attention mechanism for image captioning

Types of Attention

Using attention to improve visual models

References

Summary

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To get the most out of this book

This book is focused on building CNNs with Python programming language. We have used Python version 2.7 (2x) to build various applications and the open source and enterprise-ready professional software using Python, Spyder, Anaconda, and PyCharm. Many of the examples are also compatible with Python 3x. As a good practice, we encourage users to use Python virtual environments for implementing these codes.

We focus on how to utilize various Python and deep learning libraries (Keras, TensorFlow, and Caffe) in the best possible way to build real-world applications. In that spirit, we have tried to keep all of the code as friendly and readable as possible. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios.

Download the example code files

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

Log in or register at www.packtpub.com.
Select the SUPPORT tab.
Click on Code Downloads & Errata.
Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

WinRAR/7-Zip for Windows
Zipeg/iZip/UnRarX for Mac
7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Practical-Convolutional-Neural-Networks. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/PracticalConvolutionalNeuralNetworks_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

A block of code is set as follows:

import tensorflow as tf

#Creating TensorFlow object 
hello_constant = tf.constant('Hello World!', name = 'hello_constant')
#Creating a session object for execution of the computational graph
with tf.Session() as sess:

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

x = tf.subtract(1, 2,name=None) # -1
y = tf.multiply(2, 5,name=None) # 10

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Warnings or important notes appear like this.

Tips and tricks appear like this.

Practical Convolutional Neural Networks

By : Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari

Practical Convolutional Neural Networks

By: Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari

Overview of this book

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