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 (7 chapters)

Chapter 4. Introducing Recurrent Neural Networks

In the previous chapter, you learned about convolutional networks. Now, it's time to move on to a new type of model and problem—Recurrent Neural Networks (RNNs). In this chapter, we'll explain the workings of RNNs, and implement one in TensorFlow. Our example problem will be a simple season predictor with weather information. We will also take a look at skflow, a simplified interface to TensorFlow. This will let us quickly re-implement both our old image classification models and the new RNN. At the end of this chapter, you will have a good understanding of the following concepts:

  • Exploring RNNs
  • TensorFlow learn
  • Dense Neural Network (DNN)