Hands-On Deep Learning with Go: A practical guide to building and implementing neural network models using Go
By 作者: Gareth Seneque – Darrell Chua
ISBN-10 书号: 1789340993
ISBN-13 书号: 9781789340990
Release Finelybook 出版日期: 2019-08-08
pages 页数: (242 )
Book Description to Finelybook sorting
Apply modern deep learning techniques to build and train deep neural networks using Gorgonia
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
1 Introduction to Deep Learning in Go
2 What Is a Neural Network and How Do I Train One?
3 Beyond Basic Neural Networks – Autoencoders and RBMs
4 CUDA – GPU-Accelerated Training
5 Next Word Prediction with Recurrent Neural Networks
6 Object Recognition with Convolutional Neural Networks
7 Maze Solving with Deep Q-Networks
8 Generative Models with Variational Autoencoders
9 Building a Deep Learning Pipeline
10 Scaling Deployment