Hands-On Deep Learning with Go (notice n° 72316)
[ vue normale ]
000 -LEADER | |
---|---|
fixed length control field | 02872cam a2200289zu 4500 |
003 - CONTROL NUMBER IDENTIFIER | |
control field | FRCYB88874185 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20250107232403.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 250108s2019 fr | o|||||0|0|||eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9781789340990 |
035 ## - SYSTEM CONTROL NUMBER | |
System control number | FRCYB88874185 |
040 ## - CATALOGING SOURCE | |
Original cataloging agency | FR-PaCSA |
Language of cataloging | en |
Transcribing agency | |
Description conventions | rda |
100 1# - MAIN ENTRY--PERSONAL NAME | |
Personal name | Seneque, Gareth |
245 01 - TITLE STATEMENT | |
Title | Hands-On Deep Learning with Go |
Statement of responsibility, etc. | ['Seneque, Gareth', 'Chua, Darrell'] |
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
Name of producer, publisher, distributor, manufacturer | Packt Publishing |
Date of production, publication, distribution, manufacture, or copyright notice | 2019 |
300 ## - PHYSICAL DESCRIPTION | |
Extent | p. |
336 ## - CONTENT TYPE | |
Content type code | txt |
Source | rdacontent |
337 ## - MEDIA TYPE | |
Media type code | c |
Source | rdamdedia |
338 ## - CARRIER TYPE | |
Carrier type code | c |
Source | rdacarrier |
520 ## - SUMMARY, ETC. | |
Summary, etc. | 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. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name entry element | |
700 0# - ADDED ENTRY--PERSONAL NAME | |
Personal name | Seneque, Gareth |
700 0# - ADDED ENTRY--PERSONAL NAME | |
Personal name | Chua, Darrell |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Access method | Cyberlibris |
Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88874185">https://international.scholarvox.com/netsen/book/88874185</a> |
Electronic format type | text/html |
Host name |
Pas d'exemplaire disponible.
Réseaux sociaux