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)

Improving model performance with data augmentation

Neural networks used for image recognition not only are difficult to set up and train, they also require a lot of data to train. Also, they tend to overfit on the images used during training. For example, when you only use photos of faces in an upright position, your model will have a hard time recognizing faces that are rotated in another direction.

To help overcome problems with rotation and shifts in certain directions, you can use image augmentation. CNTK supports specific transforms when creating a minibatch source for images.

We've included an additional notebook for this chapter that demonstrates how to use the transformations. You can find the sample code for this section in the Recognizing hand-written digits with augmented data.ipynb file in the samples for this chapter.

There are several transformations that you...