Chapter 1, What is Machine Learning?, covers the fundamentals of machine learning: what supervised, unsupervised, and semi-supervised learning is and why these distinctions are important. Moreover, you will start to understand how to create a data pipeline, how to measure the performance of an algorithm, and how to validate your results.
Chapter 2, Neural Networks and Deep Learning, focuses on neural networks. You will learn about the strengths of machine learning models, how it is possible to make a network learn, and how, in practice, the model parameter update is performed. By the end of this chapter, you will understand the intuition behind backpropagation and network parameter updates. Moreover, you'll learn why deep neural network architectures are required to solve challenging tasks.
Chapter 3, TensorFlow Graph Architecture, covers the structure of TensorFlow – the structure that's shared between the 1.x and 2.x versions.
Chapter 4, TensorFlow 2.0 Architecture, demonstrates the difference between TensorFlow 1.x and TensorFlow 2.x. You'll start to develop some simple machine learning models using both these versions. You will also gain an understanding of all the common features of the two versions.
Chapter 5, Efficient Data Input Pipelines and Estimator API, shows how to define a complete data input pipeline using the tf.data API together with the use of the tf.estimator API to define experiments. By the end of this chapter, you'll be able to create complex and highly efficient input pipelines leveraging all the power of the tf.data and tf.io.gfile APIs.
Chapter 6, Image Classification Using TensorFlow Hub, covers how to use TensorFlow Hub to do transfer learning and fine-tuning easily by leveraging its tight integration with the Keras API.
Chapter 7, Introduction to Object Detection, shows how to extend your classifier, making it an object detector that regresses the coordinates of a bounding box, and also gives you an introduction to more complex object detection architectures.
Chapter 8, Semantic Segmentation and Custom Dataset Builder, covers how to implement a semantic segmentation network, how to prepare a dataset for this kind of task, and how to train and measure the performance of a model. You will solve a semantic segmentation problem using U-Net.
Chapter 9, Generative Adversarial Networks, covers GANs from a theoretical and practical point of view. You will gain an understanding of the structure of generative models and how the adversarial training can be easily implemented using TensorFlow 2.0.
Chapter 10, Bringing a Model to Production, shows how to go from a trained model to a complete application. This chapter also covers how to export a trained model to an indicated representation (SavedModel) and use it in a complete application. By the end of this chapter, you will be able to export a trained model and use it inside Python, TensorFlow.js, and also in Go using the tfgo library.