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Keras Deep Learning Cookbook

You're reading from Keras Deep Learning Cookbook Over 30 recipes for implementing deep neural networks in Python

Product type Paperback

Published in Oct 2018

Publisher Packt

ISBN-13 9781788621755

Length 252 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Authors (3):

Sujit Pal

Manpreet Singh Ghotra

Rajdeep Dua

View More author details

Table of Contents (12) Chapters

Preface

1. Keras Installation

2. Working with Keras Datasets and Models FREE CHAPTER

3. Data Preprocessing, Optimization, and Visualization

4. Classification Using Different Keras Layers

5. Implementing Convolutional Neural Networks

6. Generative Adversarial Networks

7. Recurrent Neural Networks

8. Natural Language Processing Using Keras Models

9. Text Summarization Using Keras Models

10. Reinforcement Learning

11. Other Books You May Enjoy

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Sequence padding

In this recipe, we will learn how Keras can be used for sequence padding. Padding is useful when sequences are sent in batches to the LSTM network.

Getting ready

Import the function:

from keras.preprocessing.sequence import pad_sequences

pad_sequences is a function defined as follows:

pad_sequences(sequences, maxlen=None, dtype='int32', padding='pre', truncating='pre', value=0.0)

How to do it...

Let's look at the various padding options.

...

The rest of the chapter is locked

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Authors (3)

Ghotra

Ghotra

Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.

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Dua

Dua

Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.

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Sujit Pal

Sujit Pal

Sujit Pal is a Technology Research Director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His interests include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora.

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