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)

Validating performance of a classification model

In the previous section, Choosing a good strategy to validate model performance, we talked about choosing a good strategy for validating your neural network. In the following sections, we'll dive into choosing metrics for different kinds of models.

When you're building a classification model, you're looking for metrics that express how many samples were correctly classified. You're probably also interested in measuring how many samples were incorrectly classified.

You can use a confusion matrix—a table with the predicted output versus the expected output—to find out a lot of detail about the performance of your model. This tends to get complicated, so we'll also look at a way to measure the performance of a model using the F-measure.

...