Book Image

Practical Convolutional Neural Networks

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

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.
Table of Contents (11 chapters)

What this book covers

Chapter 1, Deep Neural Networks - Overview, it gives a quick refresher of the science of deep neural networks and different frameworks that can be used to implement such networks, with the mathematics behind them.

Chapter 2, Introduction to Convolutional Neural Networks, it introduces the readers to convolutional neural networks and shows how deep learning can be used to extract insights from images.

Chapter 3, Build Your First CNN and Performance Optimization, constructs a simple CNN model for image classification from scratch, and explains how to tune hyperparameters and optimize training time and performance of CNNs for improved efficiency and accuracy respectively.

Chapter 4, Popular CNN Model Architectures, shows the advantages and working of different popular (and award winning) CNN architectures, how they differ from each other, and how to use them.

Chapter 5, Transfer Learning, teaches you to take an existing pretrained network and adapt it to a new and different dataset. There is also a custom classification problem for a real-life application using a technique called transfer learning.

Chapter 6, Autoencoders for CNN, introduces an unsupervised learning technique called autoencoders. We walk through different applications of autoencoders for CNN, such as image compression.

Chapter 7, Object Detection and Instance Segmentation with CNN, teaches the difference between object detection, instance segmentation, and image classification. We then learn multiple techniques for object detection and instance segmentation with CNNs.

Chapter 8, GAN—Generating New Images with CNN, explores generative CNN Networks, and then we combine them with our learned discriminative CNN networks to create new images with CNN/GAN.

Chapter 9, Attention Mechanism for CNN and Visual Models, teaches the intuition behind attention in deep learning and learn how attention-based models are used to implement some advanced solutions (image captioning and RAM). We also understand the different types of attention and the role of reinforcement learning with respect to the hard attention mechanism.