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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
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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

Using Gorgonia

At the time of writing this book, there are two libraries that would typically be considered for DL in Go, TensorFlow and Gorgonia. However, while TensorFlow is definitely well regarded and has a full-featured API in Python, this is not the case in Go. As discussed previously, the Go bindings for TensorFlow are only suited to loading models that have already been created in Python, but not for creating models from scratch.

Gorgonia has been built from the ground up to be a Go library that is able to both train ML models and perform inference. This is a particularly valuable property, especially if you have an existing Go application or you are looking to build a Go application. Gorgonia allows you to develop, train, and maintain your DL model in your existing Go environment. For this book, we will be using Gorgonia exclusively to build models.

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Hands-On Deep Learning with Go
Published in: Aug 2019
Publisher: Packt
ISBN-13: 9781789340990
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