1 Introduction to Deep Learning and Computer Vision 1.1 Introduction to the book 1.2 What are Deep Learning and Computer Vision 1.3 What are the use cases of implementation 1.4 Development Environment Setup for TensorFlow 1.5 Development Environment Setup for OpenCV 1.6 Development Environment Setup for Keras 2 Nuts and Bolts Nuts of Deep Learning for Computer Vision 2.1 Concepts of Image Analysis, Image Processing, etc 2.

2 Image Analysis and Processing using OpenCV 2.3 Creation of an Object Detection using OpenCV 2.4 Creating Face Detection using OpenCV 2.5 Concepts of Neural Networks introduction 2.6 Convolutional Neural Network explained in detail 2.7 Various layers of CNN explained in detail 2.8 Cats vs. dogs classification using TensorFlow and pyTorch 3 Image Classification with Convolutional Neural Network Using LeNet 3.

1 LeNet-1 Architecture 3.2 LeNet-4 Architecture 3.3 LeNet-5 architecture and Boosted LeNet architecture 3.4 MNIST Image Classification 3.5 Traffic Signs Image Classification 4 Image Classification with Convolutional Neural Network Using Other Architectures 4.1 VGG Network 4.2 AlexNet Network 4.3 Image Classification using AlexNet and VGG for CIFAR-10 datset 4.

4 Image Classification using AlexNet and VGG for Fruit Image Classification 5 Object Detection using R-CNN, Fast R-CNN, and Faster R-CNN 5.1 R-CNN 5.2 Fast R-CNN 5.3 Faster R-CNN 5.4 Object Detection using Deep Learning: concepts explained 5.5 Object Detection use cases 6 Object Tracking and Action Recognition using Convolutional Neural Network 6.1 YOLO (You Only Look Once) 6.2 SSD architecture 6.

3 What is Object Detection and concepts of Object Detection 6.4 Object tracking and recognition use cases 7 Image Creation with Generative Adversarial Networks 8 Face Recognition and Gesture Recognition 9 Human Pose Estimation 10 Image Captioning and Image Colorisation 11 Photo Styling 12 Semantic Segmentation 13 Video Analytics 14 End to Model Cycle.