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Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

By : Meints
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Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

By: 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)
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Summary

In this chapter, we learned about deep learning and its relationship to machine learning and AI. We looked at the basic concepts behind deep learning and how to train a neural network using gradient descent. We then talked about CNTK, what it is, and what features the library offers to build deep learning models. We finally spent some time discussing how to install CNTK on Windows and Linux and how to use your GPU should you want to.

In the next chapter, we will learn how to build basic neural networks with CNTK so we get a better understanding of how the concepts in this chapter work in code. We will also discuss the different ways we can use various components in our deep learning model for different scenarios.

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