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)

Working with Time Series Data

Classifying images with a neural network is one of the most iconic jobs in deep learning. But it certainly isn't the only job that neural networks excel at. Another area where there's a lot of research happening is recurrent neural networks.

In this chapter, we'll dive into recurrent neural networks, and how they can be used in scenarios where you have to deal with time series data; for example, in an IoT solution where you need to predict temperatures or other important values.

The following topics are covered in this chapter:

  • What are recurrent neural networks?
  • Usage scenarios for recurrent neural networks
  • How do recurrent neural networks work
  • Building recurrent neural networks with CNTK