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.