Go is an open source programming language designed by Google for handling large-scale projects efficiently. The Go ecosystem comprises some really powerful deep learning tools such as DQN and CUDA. With this book, you'll be able to use these tools to train and deploy scalable deep learning models from scratch. This deep learning book begins by introducing you to a variety of tools and libraries available in Go. It then takes you through building neural networks, including activation functions and the learning algorithms that make neural networks tick. In addition to this, you'll learn how to build advanced architectures such as autoencoders, restricted Boltzmann machines (RBMs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. You'll also understand how you can scale model deployments on the AWS cloud infrastructure for training and inference. By the end of this book, you'll have mastered the art of building, training, and deploying deep learning models in Go to solve real-world problems.

What you will learn

Explore the Go ecosystem of libraries and communities for deep learning Get to grips with Neural Networks, their history, and how they work Design and implement Deep Neural Networks in Go Get a strong foundation of concepts such as Backpropagation and Momentum Build Variational Autoencoders and Restricted Boltzmann Machines using Go Build models with CUDA and benchmark CPU and GPU models

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Product Details

Publication date : Aug 8, 2019

Length 242 pages

Edition : 1st Edition

Language : English

ISBN-13 : 9781789340990

Vendor :

Google

Category :

Data

Languages :

Go (Beginner)

Concepts :

Deep Learning

15 Chapters

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

1. Section 1: Deep Learning in Go, Neural Networks, and How to Train Them

Section 1: Deep Learning in Go, Neural Networks, and How to Train Them

2. Introduction to Deep Learning in Go

Introduction to Deep Learning in Go

3. What Is a Neural Network and How Do I Train One?

What Is a Neural Network and How Do I Train One?

A basic neural network

Activation functions

Gradient descent and backpropagation

Advanced gradient descent algorithms

Summary

4. Beyond Basic Neural Networks - Autoencoders and RBMs

Beyond Basic Neural Networks - Autoencoders and RBMs

Loading data – MNIST

Building a neural network for handwriting recognition

Building an autoencoder – generating MNIST digits

Building an RBM for Netflix-style collaborative filtering

Summary

Further reading

5. CUDA - GPU-Accelerated Training

CUDA - GPU-Accelerated Training

CPUs versus GPUs

Understanding Gorgonia and CUDA

Building a model in Gorgonia with CUDA support

Performance benchmarking of CPU versus GPU models for training and inference

Summary

6. Section 2: Implementing Deep Neural Network Architectures

Section 2: Implementing Deep Neural Network Architectures

7. Next Word Prediction with Recurrent Neural Networks

Next Word Prediction with Recurrent Neural Networks

Vanilla RNNs

Training RNNs

RNNs and vanishing gradients

Augmenting your RNN with GRU/LSTM units

Building an LSTM in Gorgonia

Summary

8. Object Recognition with Convolutional Neural Networks

Object Recognition with Convolutional Neural Networks

Introduction to CNNs

Building an example CNN

Assessing the results

Summary

Further reading

9. Maze Solving with Deep Q-Networks

Maze Solving with Deep Q-Networks

What is a DQN?

Solving a maze using a DQN in Gorgonia

Summary

Further reading

10. Generative Models with Variational Autoencoders

Generative Models with Variational Autoencoders

Introduction to VAEs

Building a VAE on MNIST

Assessing the results

Summary

Further reading

11. Section 3: Pipeline, Deployment, and Beyond!

Section 3: Pipeline, Deployment, and Beyond!

12. Building a Deep Learning Pipeline

Building a Deep Learning Pipeline

Exploring Pachyderm

Integrating our CNN

Summary

13. Scaling Deployment

Scaling Deployment

Lost (and found) in the cloud

Building deployment templates

Running a model on a K8s cluster

Summary

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About the authors

Darrell 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.

See other products by Darrell Chua

Gareth 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.

See other products by Gareth Seneque

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Hands-On Deep Learning with Go: A practical guide to building and implementing neural network models using Go

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