Book Image

Neural Networks with Keras Cookbook

By : V Kishore Ayyadevara
Book Image

Neural Networks with Keras Cookbook

By: V Kishore Ayyadevara

Overview of this book

This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach. We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data. Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks. We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems. Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game. By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.
Table of Contents (18 chapters)

Encoding for recommender systems

So far, in the previous sections, we have encoded an image. In this section, we will encode users and movies in a movie-related dataset. The reason for this is that there could be millions of users as customers and thousands of movies in a catalog. Thus, we are not in a position to one-hot encode such data straight away. Encoding comes in handy in such a scenario. One of the most popular techniques that's used in encoding for recommender systems is matrix factorization. In the next section, we'll understand how it works and generate embeddings for users and movies.

Getting ready

The thinking behind encoding users and movies is as follows:

If two users are similar in terms of liking...