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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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Recurrent neural networks variations

Recurrent neural networks can be used in a variety of ways. In this section we'll take a look at the different variations of recurrent neural networks and how they can be used to solve specific types of problems. Specifically we'll look at the following variations:

  • Predicting a single output based on an input sequence
  • Predicting a sequence based on a single input value
  • Predicting sequences based on other sequences

Finally we'll also explore stacking multiple recurrent neural networks together and how that helps get better performance in a scenario like processing text.

Let's take a look at the scenarios in which recurrent networks can be used, as there are several ways in which you can use the unique properties of recurrent neural networks.

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