Book Image

Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

By : Willem Meints
Book Image

Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

By: Willem Meints

Overview of this book

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
Table of Contents (9 chapters)

Basic neural network concepts in CNTK

In the previous chapter, we looked at the basic concepts of a neural network. Let's map the concepts we've learned to components in the CNTK library, and discover how you can use these concepts to build your own model.

Building neural networks using layer functions

Neural networks are made using several layers of neurons. In CNTK, we can model the layers of a neural network using layer functions defined in the layers module. A layer function in CNTK looks like a regular function. For example, you can create the most basic layer type, Dense, with one line of code:

from cntk.layers import Dense
from cntk import input_variable

features = input_variable(100)
layer = Dense(50)(features...