| Tag |
First Indicator |
Second Indicator |
Subfields |
| LEADER |
00000uam a2200000 a 4500 |
| 001 |
in00004100313 |
| 005 |
20260123215002.3 |
| 006 |
m o d |
| 007 |
cr cn |
| 008 |
040419s2019 xx o eng |
| 020 |
|
|
|z 9781789802993
|
| 020 |
|
|
|z 9781789803198
|
| 035 |
|
|
|a (CaSebORM)9781789802993
|
| 040 |
|
|
|d UtOrBLW
|
| 041 |
0 |
|
|a eng
|
| 100 |
1 |
|
|a Meints, Willem,
|e author.
|
| 245 |
1 |
0 |
|a Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide /
|c Meints, Willem.
|
| 250 |
|
|
|a 1st edition.
|
| 264 |
|
1 |
|b Packt Publishing,
|c 2019.
|
| 300 |
|
|
|a 1 online resource (208 pages)
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 347 |
|
|
|a text file
|
| 520 |
|
|
|a Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering how you can use deep learning models in your software applications with Microsoft Cognitive Toolkit Key Features Understand the fundamentals of Microsoft Cognitive Toolkit and set up the development environment Train different types of neural networks using Cognitive Toolkit and deploy it to production Evaluate the performance of your models and improve your deep learning skills Book Description Cognitive Toolkit is a very popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks, such as convolutional and recurrent neural networks. This book will help you understand the basics of deep learning. You will learn how to use Microsoft Cognitive Toolkit to build deep learning models and discover what makes this framework unique so that you know when to use it. This book will be a quick, no-nonsense introduction to the library and will teach you how to train different types of neural networks, such as convolutional neural networks, recurrent neural networks, autoencoders, and more, using Cognitive Toolkit. Then we will look at two scenarios in which deep learning can be used to enhance human capabilities. The book will also demonstrate how to evaluate your models' performance to ensure it trains and runs smoothly and gives you the most accurate results. Finally, you will get a short overview of how Cognitive Toolkit fits in to a DevOps environment What you will learn Set up your deep learning environment for the Cognitive Toolkit on Windows and Linux Pre-process and feed your data into neural networks Use neural networks to make effcient predictions and recommendations Train and deploy effcient neural networks such as CNN and RNN Detect problems in your neural network using TensorBoard Integrate Cognitive Toolkit with Azure ML Services for effective deep learning Who this book is for Data Scientists, Machine learning developers, AI developers who wish to train and deploy effective deep learning models using Microsoft CNTK will find this book to be useful. Readers need to have experience in Python or similar object-oriented language like C# or Java.
|
| 533 |
|
|
|a Electronic reproduction.
|b Boston, MA :
|c Safari,
|n Available via World Wide Web.
|d 2018.
|
| 538 |
|
|
|a Mode of access: World Wide Web.
|
| 542 |
|
|
|f Copyright © Packt Publishing
|g 2018
|
| 588 |
|
|
|a Online resource; Title from title page (viewed March 28, 2019)
|
| 500 |
|
|
|a Electronic resource.
|
| 655 |
|
7 |
|a Electronic books.
|2 local
|
| 710 |
2 |
|
|a Safari, an O'Reilly Media Company.
|
| 856 |
4 |
0 |
|u https://proxy.library.tamu.edu/login?url=https://go.oreilly.com/TAMU/library/view/-/9781789802993/?ar
|z Connect to this electronic resource
|t 0
|
| 999 |
f |
f |
|s fa82fac8-376e-3150-8b78-c04c2b670632
|i 77b79c0b-7fcf-3516-9753-f1a8f3277651
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|s www_evans
|d Available Online
|t 0
|h No information provided
|
| 998 |
f |
f |
|t 0
|l Available Online
|