Hands-On Deep Learning with Go

By : Seneque, Chua

3 (2)

Buy this Book

Hands-On Deep Learning with Go

3 (2)

By: Seneque, Chua

Buy this Book

Overview of this book

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.

Preface

Who this book is for

What this book covers

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

Introduction to Deep Learning in Go

Introducing DL

Overview of ML in Go

Using Gorgonia

Summary

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

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

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

Section 2: Implementing Deep Neural Network Architectures

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

Object Recognition with Convolutional Neural Networks

Introduction to CNNs

Building an example CNN

Assessing the results

Summary

Further reading

Maze Solving with Deep Q-Networks

What is a DQN?

Solving a maze using a DQN in Gorgonia

Summary

Further reading

Generative Models with Variational Autoencoders

Introduction to VAEs

Building a VAE on MNIST

Assessing the results

Summary

Further reading

Section 3: Pipeline, Deployment, and Beyond!

Building a Deep Learning Pipeline

Exploring Pachyderm

Integrating our CNN

Summary

Scaling Deployment

Lost (and found) in the cloud

Building deployment templates

Running a model on a K8s cluster

Summary

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Activation functions

Now that you know how to build a basic neural network, let's go through the purpose of some of the elements of your model. One of those elements was the Sigmoid, which is an activation function. Sometimes these are also called transfer functions.

As you have learned previously, a given layer can be simply defined as weights applied to inputs; add some bias and then decide on activation. An activation function decides whether a neuron is fired. We also put this into the network to help to create more complex relationships between input and output. While doing this, we also need it to be a function that works with our backpropagation, so that we can easily optimize our weighs via an optimization method (that is, gradient descent). This means that we need the output of the function to be differentiable.

There are a few things to consider when choosing an...