This is the code repository for Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide, published by Packt.
A practical guide to building neural networks using Microsoft's open source deep learning framework
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 covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
from cntk.layers import Dense
from cntk import input_variable
features = input_variable(50)
layer = Dense(50)(features)
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-7).
| Chapter | Software required | OS required |
|---|---|---|
| 1-7 | Anaconda 2018.2, CNTK 2.6, .NET Core 2.2 | Windows, Mac OS X, and Linux (Any) |
Click on the following link to see the Code in Action:
Willem Meints is a software architect and engineer with a wide variety of interests. His background in software engineering hasn't stopped him from exploring new areas, such as machine learning, as part of his daily work. This sparked a deep passion for everything related to artificial intelligence and deep learning.
Willem studied electronics after his high school career, but quickly discovered he had more fun building applications. This led to his decision to leave the world of electronics and launch a career in software engineering. After he finished his bachelor's degree in software engineering, he started working for Info Support, where he's been working ever since.
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