Book Image

Deep Learning Quick Reference

By : Mike Bernico
Book Image

Deep Learning Quick Reference

By: Mike Bernico

Overview of this book

Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks. By the end of this book, you will be able to solve real-world problems quickly with deep neural networks.
Table of Contents (15 chapters)

Training an RNN from scratch

Recurrent neural networks (RNNs) are a group of neural networks that are built to model sequential data. In the last few chapters, we looked at using convolutional layers to learn features from images. Recurrent layers are equally as useful when we want to learn features from a sequence of values that are all related: xt, xt-1, xt-2, xt-3.

In this chapter, we will talk about how to use RNNs for time series problems, which are unsurprisingly problems involving a sequence of data points placed in temporal or chronological order.

We will cover the following topics in this chapter:

  • Introducing recurrent neural networks
  • Time series problems
  • Using an LSTM for time series prediction