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)

Summary

In this chapter, we've explored how you can train your neural networks with both small and large datasets. For smaller datasets, we've looked at how you can quickly train a model by calling the train method on the loss function. For larger datasets, we've explored how you can use both MinibatchSource and a manual minibatch loop to train your network.

Using the right method of training can make a big difference in how long it takes to train your model and how good your model will be in the end. You can now make an informed choice between using in-memory data and reading data in chunks. Make sure you experiment with the minibatch size settings to see what works best for your model.

Up until this chapter, we haven't looked at methods to monitor your model. We did see some fragments with a progress writer to help you visualize the training process. But...