Chapter 1, Setting Up an Anaconda Environment, helps you download and install Python 3 and Anaconda along with their additional libraries, and also discusses the basic concepts of Jupyter Notebook.
Chapter 2, Image Captioning with TensorFlow, introduces you to image captioning using the Google Brain im2txt captioning model, which is a pre-defined model. We will also learn the process of retraining the model for our own customized images.
Chapter 3, Reading License Plates with OpenCV, introduces you to reading license plates using the plate utility functions. We learn the process of finding the possible candidates for our license plate characters, which is key to reading license plates.
Chapter 4, Human Pose Estimation with TensorFlow, introduces you to pose estimation using the DeeperCut algorithm and the pre-defined ArtTrack model. You will learn about single-person and multi-person pose detection, and you'll learn how to retrain the model for images and videos.
Chapter 5, Handwritten Digit Recognition with scikit-learn and TensorFlow, helps you acquire and process MNIST digit data. You will learn how to create and train a support vector machine, and also learn about digit classification using TensorFlow.
Chapter 6, Facial Feature Tracking and Classification with dlib, helps you detect facial features from images and videos, which helps us carry out facial recognition.
Chapter 7, Deep Learning Image Classification with TensorFlow, helps you learn image classification using a pre-trained Inception model. The chapter also teaches you how to retrain the model for customized images.