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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

Object Recognition with Convolutional Neural Networks

Now it's time to get to some computer vision or image classification problems that are a little more general than our earlier MNIST handwriting example. A lot of the same principles apply, but we will be using some new types of operations to build Convolutional Neural Networks (CNNs).

This chapter will cover the following topics:

  • Introduction to CNNs
  • Building an example CNN
  • Assessing the results and making improvements
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