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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

Loading data – MNIST

Before we can even begin to train or build our model, we first need to get some data. As it turns out, a lot of people have made data available online for us to use for this purpose. One of the best-curated datasets around is MNIST, which we will use for the first two examples in this chapter.

We'll learn how to download MNIST and load it into our Go program so that we can use it in our model.

What is MNIST?

Throughout this chapter, we're going to make use of a popular dataset called the MNIST database. This has been made available by Yann LeCun, Corinna Cortes, and Christopher Burges at http://yann.lecun.com/exdb/mnist.

The database gets its name from the fact that it was made by mixing...

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