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You're reading from Hands-On Deep Learning with Go A practical guide to building and implementing neural network models using Go

Product type Paperback

Published in Aug 2019

Publisher Packt

ISBN-13 9781789340990

Length 242 pages

Edition 1st Edition

Languages

Tools

CUDA

Concepts

Deep Learning

Authors (2):

Darrell Chua

Gareth Seneque

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Table of Contents (15) Chapters

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

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Building deployment templates

We will now put together the various templates required to deploy and train our model at scale. These templates include:

AWS cloud formation templates: Virtual instances and related resources
Kubernetes or KOPS configuration: K8s cluster management
Docker templates or Makefile: Create images to deploy on our K8s cluster

We are choosing a particular path here. AWS has services such as Elastic Container Service (ECS) and Elastic Kubernetes Service (EKS) that are accessible via simple API calls. Our purpose here is to engage with the nitty-gritty details, so that you can make informed choices about how to scale the deployment of your own use case. For now, you have greater control over container options and how processing is distributed, as well as how your model is called when deploying containers to a vanilla EC2 instance. These services are also...

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Authors (2)

Seneque

Gareth Seneque is a machine learning engineer with 11 years' experience of building and deploying systems at scale in the finance and media industries. He became interested in deep learning in 2014 and is currently building a search platform within his organization, using neuro-linguistic programming and other machine learning techniques to generate content metadata and drive recommendations. He has contributed to a number of open source projects, including CoREBench and Gorgonia. He also has extensive experience with modern DevOps practices, using AWS, Docker, and Kubernetes to effectively distribute the processing of machine learning workloads.

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Chua

Darrell Chua is a senior data scientist with more than 10 years' experience. He has developed models of varying complexity, from building credit scorecards with logistic regression to creating image classification models for trading cards. He has spent the majority of his time working with in fintech companies, trying to bring machine learning technologies into the world of finance. He has been programming in Go for several years and has been working on deep learning models for even longer. Among his achievements is the creation of numerous business intelligence and data science pipelines that enable the delivery of a top-of-the-line automated underwriting system, producing near-instant approval decisions.

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