Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide: A practical guide to building neural networks using Microsoft*s open source deep learning framework

Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering how you can use deep learning models in your software applications with Microsoft Cognitive Toolkit

Key Features

• Understand the fundamentals of Microsoft Cognitive Toolkit and set up the development environment
• Train different types of neural networks using Cognitive Toolkit and deploy it to production
• Evaluate the performance of your models and improve your deep learning skills

Book Description

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

What you will learn

• Set up your deep learning environment for the Cognitive Toolkit on Windows and Linux
• Pre-process and feed your data into neural networks
• Use neural networks to make effcient predictions and recommendations
• Train and deploy effcient neural networks such as CNN and RNN
• Detect problems in your neural network using TensorBoard
• Integrate Cognitive Toolkit with Azure ML Services for effective deep learning

Who this book is for

Data Scientists, Machine learning developers, AI developers who wish to train and deploy effective deep learning models using Microsoft CNTK will find this book to be useful. Readers need to have experience in Python or similar object-oriented language like C# or Java.

Table of Contents

1. Getting Started with CNTK
2. Building Neural Networks with CNTK
3. Getting Data into Your Neural Network
4. Validating Model Performance
5. Working with Images
6. Working with Time Series Data
7. Deploying Models to Production