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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

Integrating our CNN

We will now take our CNN example from an earlier chapter and make some updates that are necessary to package and deploy the network using data supplied by Pachyderm.

Creating a Docker image of our CNN

Pachyderm data pipelines are dependent on prebaked Docker images. The internet is full of Docker tutorials, so we'll keep things simple here and discuss what we need to do to take advantage of the simple deployment steps for any Go application.

Let's take a look at our Dockerfile:

FROM golang:1.12

ADD main.go /main.go

ADD cifar/ /cifar/

RUN export GOPATH=$HOME/go && cd / && go get -d -v .

And that's it! We're simply fetching the Go 1.12 image from Docker Hub and dropping our...

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