Book Image

Hands-On Deep Learning with TensorFlow

By : Dan Van Boxel
Book Image

Hands-On Deep Learning with TensorFlow

By: Dan Van Boxel

Overview of this book

Dan Van Boxel’s Deep Learning with TensorFlow is based on Dan’s best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data. With Dan’s guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.
Table of Contents (12 chapters)

The multiple hidden layer model

In this section, we'll show you how to build even more complex models with additional hidden layers. We'll adapt our single hidden layer model into a multilayer model known as a deep neural network. Then, we'll discuss choosing how many neurons and layers to use. Finally, we'll train the model itself, being patient, as this might take a while to compute.

Remember when we added a hidden layer of neurons to our logistic regression model? Well, we can do that again, adding another layer to our single hidden layer model. Once you have more than one layer of neurons, we call this a deep neural network. However, everything you learned before can be applied now. As in the previous sections of this chapter, you should make a fresh Python session and execute the code up to num_hidden1 in this section's code file. Then the fun starts.

Exploring the multiple hidden layer model

Let's start by changing the old num_hidden to num_hidden1 to indicate the number of neurons on...