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)

Summary

In this chapter, we talked about using recurrent neural networks to predict the next element in a sequence. We covered both RNNs in general and LSTMs specifically, and we focused on using LSTMs to predict a time series. In order to make sure we understood the benefits and challenges of using LSTMs for time series, we briefly reviewed some basics of time series analysis. We spent a few minutes talking about traditional time series models as well, including ARIMA and ARIMAX.

Lastly, we walked through a challenging use case where we used an LSTM to predict the price of a bitcoin.

In the next chapter, we will continue to use RNNs, now focusing on natural language processing tasks and introducing the concept of embedding layers.