Book Image

Keras Deep Learning Cookbook

By : Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra
Book Image

Keras Deep Learning Cookbook

By: Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

Overview of this book

Keras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy. The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

Word embedding

Word embedding is an NLP technique for representing words and documents using a dense vector representation compared to the bag of word techniques, which used a large sparse vector representation. Embeddings are a class of NLP methods that aim to project the semantic meaning of words into a geometric space. This is accomplished by linking a numeric vector to each word in a dictionary so that the distance between any two vectors captures the part of the semantic relationship between the two associated words. The geometric space formed by these vectors is called an embedding space.

The two most popular techniques for learning word embeddings are global vectors for word representation (GloVe) and word to vector representation (Word2vec).

In the following sections, we will be processing sample documents through the neural network with and without the embedding layer.

Getting ready

In the first case, we will not use any pre-trained word embeddings from Keras. Keras provides an embedding...