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

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Table of Contents (9 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Getting Started with CNTK
Getting Started with CNTK
The relationship between AI, machine learning, and deep learning
How does deep learning work?
What is CNTK?
Installing CNTK
Summary
2
Building Neural Networks with CNTK
Building Neural Networks with CNTK
Technical requirements
Basic neural network concepts in CNTK
Building your first neural network
Training the neural network
Making predictions with a neural network
Improving the model
Summary
3
Getting Data into Your Neural Network
Getting Data into Your Neural Network
Technical requirements
Training a neural network efficiently with minibatches
Working with small in-memory datasets
Working with large datasets
Taking control over the minibatch loop
Summary
4
Validating Model Performance
Validating Model Performance
Technical requirements
Choosing a good strategy to validate model performance
Validating performance of a classification model
Validating performance of a regression model
Measuring performance for out-of-memory datasets
Monitoring your model
Summary
5
Working with Images
Working with Images
Technical requirements
Convolutional neural network architecture
Building convolutional networks
Improving model performance with data augmentation
Summary
6
Working with Time Series Data
Working with Time Series Data
Technical requirements
What are recurrent neural networks?
Recurrent neural networks variations
How do recurrent neural networks work?
Building recurrent neural networks with CNTK
Summary
7
Deploying Models to Production
Deploying Models to Production
Technical requirements
Using machine learning in a DevOps environment
Storing your models
Using Azure Machine Learning service to manage models
Summary
8
Other Books You May Enjoy
Other Books You May Enjoy
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Building recurrent neural networks with CNTK

Now that we've explored the theory behind recurrent neural networks, it's time to build one with CNTK. There are several building blocks that CNTK offers for building recurrent neural networks. We're going to explore how to build a recurrent neural network using a sample dataset containing power measurements from a solar panel.

The power output of a solar panel changes during the day, so it's hard to predict how much power is generated for a typical house. This makes it hard for a local energy company to predict how much additional power they should generate to keep up with demand.

Luckily, many energy companies offer software that allows customers to keep track of the power output of their solar panels. This will allow them to train a model based on this historical data, so we can predict what the total power...

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