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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
Languages
Tools
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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

Exploring Pachyderm

Our focus for this book is on developing deep learning systems in Go. So, naturally, now that we are talking about how to manage the data that we feed to our networks, let's take a look at a tool to do so that is also written in Go.

Pachyderm is a mature and scalable tool that offers containerized data pipelines. In these, everything you could possibly need, from data to tools, is held together in a single place where deployments can be maintained and managed and versioning for the data itself. The Pachyderm team sell their tool as Git for data, which is a useful analogy. Ideally, we want to version the entire pipeline so that we know which data was used to train, and which, in turn, gave us the specific prediction of X.

Pachyderm removes much of the complexity of managing these pipelines. Both Docker and Kubernetes run under the hood. We will explore...

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