Chapter 1, Getting Started with TensorFlow, covers some basic concepts that will be found in all the subsequent chapters. We’ll introduce machine learning and deep learning architectures. Finally, we’ll introduce deep learning architectures, the so-called Deep Neural Networks: these are distinguished from the more commonplace single-hidden-layer neural networks by their depth; that is, the number of node layers through which data passes in a multistep process of pattern recognition. We will provide a comparative analysis of deep learning architectures with a chart summarizing all the neural networks from where most of the deep learning algorithm evolved.
Chapter 2, First Look at TensorFlow, will cover the main features and capabilities of TensorFlow 1.x: getting started with computation graph, data model, programming model and TensorBoard. In the last part of the chapter, we’ll see TensorFlow in action by implementing a Single Input Neuron. Finally, it will show how to upgrade from TensorFlow 0.x to TensorFlow 1.x.
Chapter 3, Using TensorFlow on a Feed-Forward Neural Network, provides a detailed introduction of feed-forward neural networks. The chapter will be also very practical, implementing a lot of application examples using this fundamental architecture.
Chapter 4, TensorFlow on a Convolutional Neural Network, introduces the CNNs networks that are the basic blocks of a deep learning-based image classifier. We’ll develop two examples of CNN networks; the first is the classic MNIST digit classification problem, while the purpose for the second is to train a network on a series of facial images to classify their emotional stretch.
Chapter 5, Optimizing TensorFlow Autoencoders, presents autoencoders networks that are designed and trained for transforming an input pattern so that, in the presence of a degraded or incomplete version of an input pattern, it is possible to obtain the original pattern. In the chapter, we’ll see autoencoders in action with some application examples.
Chapter 6, Recurrent Neural Networks, explains this fundamental architecture designed to handle data that comes in different lengths, that is very popular for various natural language processing tasks. Text processing and image classification problems will be implemented in the course if this chapter.
Chapter 7, GPU Computing, shows the TensorFlow facilities for GPU computing. In this chapter, we’ll explore some techniques to handle GPU using TensorFlow.
Chapter 8, Advanced TensorFlow Programming, gives an overviewof the following TensorFlow-based libraries: Keras, Pretty Tensor, and TFLearn. For each library, we’ll describe the main features with an application example.
Chapter 9, Advanced Multimedia Programming with TensorFlow, covers some advanced and emerging aspects of multimedia programming using TensorFlow. Deep neural networks for scalable object detection and deep learning on Android using TensorFlow with an example with the code will be discussed. The Accelerated Linear Algebra (XLA) and Keras will be discussed with examples to make the discussion more concrete.
Chapter 10, Reinforcement Learning, covers the basic concepts of RL. We will experience the Q-learning algorithm that is one of the most popular reinforcement learning algorithms. Furthermore, we’ll introduce the OpenAI gym framework that is a TensorFlow compatible, toolkit for developing and comparing reinforcement learning algorithms.
