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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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Toc

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

Summary

In this chapter, we covered how to build a simple neural network and how to inspect your graph, as well as many of the commonly used activation functions. We then covered the basics of how a neural network is trained via backpropagation and gradient descent. Finally, we discussed some of the different options for gradient descent algorithms and optimizations for your neural network.

The next chapter will cover building a practical feedforward neural network and autoencoders, as well as Restricted Boltzmann Machines (RBMs).

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