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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 FREE CHAPTER
2. Introduction to Deep Learning in Go 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 have gotten practical and looked at what is involved in starting the augmentation of input or output components of your model, and what tools we can use to do that in a maintainable and traceable way. At a high level, we learned about what a data pipeline is and why it is important, how to build/deploy/maintain pipelines in Pachyderm, and what tools to use to visualize our repositories and pipelines.

In the next chapter, we will look at a number of the technologies that sit underneath Pachyderm, including Docker and Kubernetes, and how we can use these tools to deploy stacks to the cloud infrastructure.

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