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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 a model in Gorgonia with CUDA support

Building a model in Gorgonia with CUDA support that we do a few things first. We need to install Gorgonia's cu interface to CUDA, and then have a model ready to train!

Installing CUDA support for Gorgonia

To make use of CUDA, you need a computer with a GPU made by NVIDIA. Unfortunately, setting up CUDA to work with Gorgonia is a slightly more involved process, as it involves setting up a C compiler environment to work with Go, as well as a C compiler environment that works with CUDA. NVIDIA has kindly ensured that its compiler works with the common toolchain for each platform: Visual Studio on Windows, Clang-LLVM on macOS, and GCC on Linux.

Installing CUDA and ensuring that...

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