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)

Building Neural Networks with CNTK

In the previous chapter, we talked about what deep learning is, and how neural networks work on a conceptual level. Finally, we talked about CNTK, and how to get it installed on your machine. In this chapter, we will build our first neural network with CNTK and train it.

We will look at building a neural network using the different functions and classes from the CNTK library. We will do this with a basic classification problem.

Once we have a neural network for our classification problem, we will train it with sample data obtained from an open dataset. After our neural network is trained, we will look at how to use it to make predictions.

At the end of this chapter, we will spend some time talking about ways to improve your model once you've trained it.

In this chapter, we will cover the following topics:

  • Basic neural network concepts...