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Python Deep Learning

You're reading from Python Deep Learning Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

Product type Paperback

Published in Jan 2019

Publisher Packt

ISBN-13 9781789348460

Length 386 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Authors (5):

Vasilev

Daniel Slater

Spacagna

Roelants

Zocca

+1 more

View More author details

Table of Contents (16) Chapters

Title Page

About Packt

Contributors

Preface

1. Machine Learning - an Introduction FREE CHAPTER

2. Neural Networks

3. Deep Learning Fundamentals

4. Computer Vision with Convolutional Networks

5. Advanced Computer Vision

6. Generating Images with GANs and VAEs

7. Recurrent Neural Networks and Language Models

8. Reinforcement Learning Theory

9. Deep Reinforcement Learning for Games

10. Deep Learning in Autonomous Vehicles

1. Other Books You May Enjoy

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Index

Chapter 7. Recurrent Neural Networks and Language Models

The neural network architectures we discussed in the previous chapters take in fixed sized input and provide fixed sized output. This chapter will lift this constraint by introducing Recurrent Neural Networks (RNNs). RNNs help us deal with sequences of variable length by defining a recurrence relation over these sequences (hence the name).

The ability to process arbitrary sequences of input makes RNNs applicable for natural language processing (NLP) and speech recognition tasks. In fact, RNNs can be applied to any problem since it has been proven that they are Turing complete – theoretically, they can simulate any program that a regular computer would not be able to compute. For example, Google's DeepMind has proposed a model called Differentiable Neural Computer, which can learn how to execute simple algorithms, such as sorting.

In this chapter, we will cover the following topics:

Recurrent neural networks
Language modeling
Sequence to...

The rest of the chapter is locked

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Authors (5)

Vasilev

Vasilev

Ivan Vasilev started working on the first open source Java deep learning library with GPU support in 2013. The library was acquired by a German company, with whom he continued its development. He has also worked as a machine learning engineer and researcher in medical image classification and segmentation with deep neural networks. Since 2017, he has focused on financial machine learning. He co-founded an algorithmic trading company, where he's the lead engineer. He holds an MSc in artificial intelligence from Sofia University St. Kliment Ohridski and has written two previous books on the same topic.

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Daniel Slater

Daniel Slater

Daniel Slater started programming at age 11, developing mods for the id Software game Quake. His obsession led him to become a developer working in the gaming industry on the hit computer game series Championship Manager. He then moved into finance, working on risk- and high-performance messaging systems. He now is a staff engineer working on big data at Skimlinks to understand online user behavior. He spends his spare time training AI to beat computer games. He talks at tech conferences about deep learning and reinforcement learning; and the name of his blog is Daniel Slater's blog. His work in this field has been cited by Google.

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Spacagna

Spacagna

Gianmario Spacagna is a senior data scientist at Pirelli, processing sensors and telemetry data for internet of things (IoT) and connected-vehicle applications. He works closely with tire mechanics, engineers, and business units to analyze and formulate hybrid, physics-driven, and data-driven automotive models. His main expertise is in building ML systems and end-to-end solutions for data products. He holds a master's degree in telematics from the Polytechnic of Turin, as well as one in software engineering of distributed systems from KTH, Stockholm. Prior to Pirelli, he worked in retail and business banking (Barclays), cyber security (Cisco), predictive marketing (AgilOne), and did some occasional freelancing.

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Roelants

Roelants

Peter Roelants holds a master's in computer science with a specialization in AI from KU Leuven. He works on applying deep learning to a variety of problems, such as spectral imaging, speech recognition, text understanding, and document information extraction. He currently works at Onfido as a team leader for the data extraction research team, focusing on data extraction from official documents.

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Zocca

Zocca

Valentino Zocca has a PhD degree and graduated with a Laurea in mathematics from the University of Maryland, USA, and University of Rome, respectively, and spent a semester at the University of Warwick. He started working on high-tech projects of an advanced stereo 3D Earth visualization software with head tracking at Autometric, a company later bought by Boeing. There he developed many mathematical algorithms and predictive models, and using Hadoop he automated several satellite-imagery visualization programs. He has worked as an independent consultant at the U.S. Census Bureau, in the USA and in Italy. Currently, Valentino lives in New York and works as an independent consultant to a large financial company.

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