Hands-On Deep Learning with Go
Idioma: inglés
Editorial: Packt Publishing Limited, GB, 2019
- Tapa blanda
- Nuevo
Librería: Rarewaves.com USA, London, London, Reino UnidoRarewaves.com USA
Vendedor de IberLibro desde 11 de junio de 2025
Condición: Nuevo
EUR 59,15
Cantidad disponible: Más de 20 disponibles
Añadir al carritoDescripción del artículo del vendedor
Apply modern deep learning techniques to build and train deep neural networks using GorgoniaKey FeaturesGain a practical understanding of deep learning using GolangBuild complex neural network models using Go libraries and GorgoniaTake your deep learning model from design to deployment with this handy guideBook DescriptionGo 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 learnExplore the Go ecosystem of libraries and communities for deep learningGet to grips with Neural Networks, their history, and how they workDesign and implement Deep Neural Networks in GoGet a strong foundation of concepts such as Backpropagation and MomentumBuild Variational Autoencoders and Restricted Boltzmann Machines using GoBuild models with CUDA and benchmark CPU and GPU modelsWho this book is forThis book is for data scientists, machine learning engineers, and AI developers who want to build state-of-the-art deep learning models using Go. Familiarity with basic machine learning concepts and Go programming is required to get the best out of this book.…
N° de ref. del artículo LU-9781789340990
- Título
- Hands-On Deep Learning with Go
- Autor
- Gareth Seneque, Darrell Chua
- Editorial
- Packt Publishing Limited, GB
- Año de publicación
- 2019
- Estado
- New
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 10
- 1789340993
- ISBN 13
- 9781789340990
Apply modern deep learning techniques to build and train deep neural networks using Gorgonia
Key Features
- Gain a practical understanding of deep learning using Golang
- Build complex neural network models using Go libraries and Gorgonia
- Take your deep learning model from design to deployment with this handy guide
Book Description
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
Who this book is for
This book is for data scientists, machine learning engineers, and AI developers who want to build state-of-the-art deep learning models using Go. Familiarity with basic machine learning concepts and Go programming is required to get the best out of this book.
Table of Contents
- Introduction to Deep Learning in Go
- What Is a Neural Network and How Do I Train One?
- Beyond Basic Neural Networks - Autoencoders and RBMs
- CUDA - GPU-Accelerated Training
- Next Word Prediction with Recurrent Neural Networks
- Object Recognition with Convolutional Neural Networks
- Maze Solving with Deep Q-Networks
- Generative Models with Variational Autoencoders
- Building a Deep Learning Pipeline
- Scaling Deployment
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
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.
“Acerca de” puede pertenecer a otra edición de este título.
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