Book Image

The TensorFlow Workshop

By : Matthew Moocarme, Abhranshu Bagchi, Anthony So, Anthony Maddalone
Book Image

The TensorFlow Workshop

By: Matthew Moocarme, Abhranshu Bagchi, Anthony So, Anthony Maddalone

Overview of this book

Getting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging. If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it’ll quickly get you up and running. You’ll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you’ll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models. Building on this solid foundation, you’ll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing. By the end of this deep learning book, you’ll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow.
Table of Contents (13 chapters)
Preface

Implementing Artificial Neural Networks in TensorFlow

The advanced flexibility that TensorFlow offers lends itself well to creating artificial neural networks (ANNs). ANNs are algorithms that are inspired by the connectivity of neurons in the brain and are intended to replicate the process in which humans learn. They consist of layers through which information propagates from the input to the output.

Figure 1.1 shows a visual representation of an ANN. An input layer is on the left-hand side, which, in this example, has two features (X1 and X2). The input layer is connected to the first hidden layer, which has three units. All the data from the previous layer gets passed to each unit in the first hidden layer. The data is then passed to the second hidden layer, which also has three units. Again, the information from each unit of the prior layer is passed to each unit of the second hidden layer. Finally, all the information from the second hidden layer is passed to the output layer...