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Hands-On Deep Learning with Go

You're reading from   Hands-On Deep Learning with Go A practical guide to building and implementing neural network models using Go

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789340990
Length 242 pages
Edition 1st Edition
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Authors (2):
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Darrell Chua Darrell Chua
Author Profile Icon Darrell Chua
Darrell Chua
Gareth Seneque Gareth Seneque
Author Profile Icon Gareth Seneque
Gareth Seneque
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Deep Learning in Go, Neural Networks, and How to Train Them
2. Introduction to Deep Learning in Go FREE CHAPTER 3. What Is a Neural Network and How Do I Train One? 4. Beyond Basic Neural Networks - Autoencoders and RBMs 5. CUDA - GPU-Accelerated Training 6. Section 2: Implementing Deep Neural Network Architectures
7. Next Word Prediction with Recurrent Neural Networks 8. Object Recognition with Convolutional Neural Networks 9. Maze Solving with Deep Q-Networks 10. Generative Models with Variational Autoencoders 11. Section 3: Pipeline, Deployment, and Beyond!
12. Building a Deep Learning Pipeline 13. Scaling Deployment 14. Other Books You May Enjoy

Building an LSTM in Gorgonia

Now that we've discussed what RNNs are, how to train them, and how to modify them for improved performance, let's build one! The next few sections will cover how we process and represent data for an RNN that uses LSTM units. We will also look at what the network itself looks like, the code for GRU units, and some tools for understanding what our network is doing, too.

Representing text data

While our aim is to predict the next word in a given sentence, or (ideally) predict a series of words that make sense and conform to some measure of English syntax/grammar, we will actually be encoding our data at the character level. This means that we need to take our text data (in this example,...

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