What this book covers

Chapter 1, Setup and Introduction to Tensorflow, covers the setting up and installation of TensorFlow along with writing a simple Tensorflow model for machine learning.

Chapter 2, Deep Learning and Convolutional Neural Networks, introduces you to machine learning, and artificial intelligence as well as artificial neural networks and how to train them. It also covers CNNs and how to use TensorFlow to train your own CNN.

Chapter 3, Image Classification in Tensorflow, talks about building CNN models and how to train them for classifying the CIFAR10 dataset. It also looks at ways to help improve the quality of our trained model by talking about different methods of initialization and regularization.

Chapter 4, Object Detection and Segmentation, teaches the basics of object localization, detection and segmentation and the most famous algorithms related to those topics.

Chapter 5, VGG, Inception Modules, Residuals, and MobileNets, introduces you to different convolutional neural network designs like VGGNet, GoggLeNet, and MobileNet.

Chapter 6, AutoEncoders, Variational Autoencoders, and Generative Adversarial Networks, introduces you to generative models, generative adversarial network, and different types of encoders. 

Chapter 7, Transfer Learning, covers the usage of transfer learning and implementing it in our own tasks.

Chapter 8, Machine Learning Best Practices and Troubleshooting, introduces us to preparing and splitting a dataset into subsets and performing meaningful tests. The chapter also talks about underfitting and overfitting along with the best practices for addressing them.

Chapter 9, Training at Scale, teaches you how to train TensorFlow models across multiple GPUs and machines. It also covers best practices for storing your data and feeding it to your model.