Data
Reader small image

You're reading from  Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

Product typeBook
Published inMar 2019
Reading LevelIntermediate
PublisherPackt
ISBN-139781789802993
Edition1st Edition
Languages
Python
Tools
Microsoft Cognitive Services
Concepts
Deep Learning
Right arrow
Author (1)
Willem Meints
Willem Meints
author image
Willem Meints

Willem Meints is a software architect and engineer with a wide variety of interests. His background in software engineering hasnt stopped him from exploring new areas like machine learning as part of his daily work. This sparked a deep passion for everything related to artificial intelligence and deep learning. Willem studied electronics after his high-school career but quickly discovered he had more fun building applications. This led to his decision to leave the world of electronics and find a career in software engineering. After he finished his bachelor in software engineering he started working for Info Support where hes been working ever since.
Read more about Willem Meints

Right arrow

Technical requirements

We assume that you have a recent version of Anaconda installed on your computer, and have followed the steps in Chapter 1, Getting Started with CNTK, to install CNTK on your computer. The sample code for this chapter can be found in our GitHub repository at https://github.com/PacktPublishing/Deep-Learning-with-Microsoft-Cognitive-Toolkit-Quick-Start-Guide/tree/master/ch6.

In this chapter, we'll work on an example stored in Jupyter notebooks. To access the sample code, run the following commands inside an Anaconda prompt in the directory where you've downloaded the code:

cd ch6
jupyter notebook

The sample code is stored in the Training recurrent neural networks.ipynb notebook. Please be aware that running the sample code for this chapter will take a long time if you don't have a machine with a GPU that can be used by CNTK.

Check out the following...

lock icon
The rest of the page is locked
Previous Page
Chapter 7 of 9, Page 2
Next Page
You have been reading a chapter from
Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide
Published in: Mar 2019Publisher: PacktISBN-13: 9781789802993
© 2019 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Willem Meints

Willem Meints is a software architect and engineer with a wide variety of interests. His background in software engineering hasnt stopped him from exploring new areas like machine learning as part of his daily work. This sparked a deep passion for everything related to artificial intelligence and deep learning. Willem studied electronics after his high-school career but quickly discovered he had more fun building applications. This led to his decision to leave the world of electronics and find a career in software engineering. After he finished his bachelor in software engineering he started working for Info Support where hes been working ever since.
Read more about Willem Meints

Other recommended products

Related to this chapter

Recurrent Neural Networks with Python Quick Start Guide

Recurrent Neural Networks with Python Quick Start Guide

Developers struggle to find an easy to follow learning resource for implementing Recurrent Neural Network(RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve the results. This book will teach you the fundamentals of RNNs with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling.

BookNov 2018122 pages
Hands-On Machine Learning with Azure

Hands-On Machine Learning with Azure

This book will teach you how advanced machine learning can be performed in the cloud in a very cheap way. You will learn more about Azure ML processes as an enterprise-ready methodology. By the end of this book, you will implement machine learning and artificial intelligence concepts in your model to solve real-world problems.

BookOct 2018340 pages
Caffe2 Quick Start Guide

Caffe2 Quick Start Guide

Caffe2 by Facebook is a popular and relatively lightweight deep learning framework. Caffe2 is known for speed, accuracy and high efficiency in training neural networks. Caffe2 is widely used in mobile apps. This book is a fast paced guide that will teach you how to train and deploy deep learning models with Caffe2 on resource constrained platforms.

BookMay 2019136 pages
Mastering Predictive Analytics with scikit-learn and TensorFlow

Mastering Predictive Analytics with scikit-learn and TensorFlow

In this book, you will find a range of methods to improve the performance of almost any predictive model, from ensemble methods to dimensionality reduction and cross-validation. You will learn the tools to produce advanced predictive models. In addition, you will dive into the exiting field of Deep Learning using TensorFlow.

BookSep 2018154 pages
Applied Deep Learning with PyTorch

Applied Deep Learning with PyTorch

Starting with the basics of deep learning and their various applications, Applied Deep Learning with PyTorch shows you how to solve trending tasks, such as image classification and natural language processing by understanding the different architectures of the neural networks.

BookApr 2019254 pages
Hands-On Natural Language Processing with PyTorch 1.x

Hands-On Natural Language Processing with PyTorch 1.x

Developers working with NLP will be able to put their knowledge to work with this practical guide to PyTorch. You will learn to use PyTorch offerings and how to understand and analyze text using Python. You will learn to extract the underlying meaning in the text using deep neural networks and modern deep learning algorithms.

BookJul 2020276 pages
Python Deep Learning Cookbook

Python Deep Learning Cookbook

Deep Learning is a rapidly evolving field of Machine Learning science which gives machines the ability to learn from information. This book contains detailed recipes to tackle with the common and not so common problems while dealing with deep learning algorithms and models in Python. You will benefit from this book by finding technical solutions to the issues presented, along with a detailed explanation of the solutions, and a discussion on corresponding pros and cons of implementing the proposed solution using Theano, Tensorflow, MXNet, and Keras. You'll come across recipes on data pre-processing, network models and topologies, supervised and unsupervised learning presented in a “solution to problem” fashion.

BookOct 2017330 pages
The Deep Learning with PyTorch Workshop

The Deep Learning with PyTorch Workshop

With this hands-on, self-paced guide, you'll explore crucial deep learning topics and discover the structure and syntax of PyTorch. Challenging activities and interactive exercises will keep you motivated and encourage you to build intelligent applications effectively.

BookJul 2020330 pages
Hands-On Deep Learning Algorithms with Python

Hands-On Deep Learning Algorithms with Python

This book introduces basic-to-advanced deep learning algorithms used in a production environment by AI researchers and principal data scientists; it explains algorithms intuitively, including the underlying math, and shows how to implement them using popular Python-based deep learning libraries such as TensorFlow.

BookJul 2019512 pages
Beginning Application Development with TensorFlow and Keras

Beginning Application Development with TensorFlow and Keras

With this book, you’ll learn how to train, evaluate and deploy Tensorflow and Keras models as real-world web applications. After a hands-on introduction, you’ll use a sample model to explore the details of deep learning, selecting the right layers that can solve a given problem. By the end of the book, you’ll build a Bitcoin application that predicts the future price, based on historic, and freely available information.

BookMay 2018148 pages
Deep Learning with PyTorch

Deep Learning with PyTorch

This book provides the intuition behind the state of the art Deep Learning architectures such as ResNet, DenseNet, Inception, and encoder-decoder without diving deep into the math of it. It shows how you can implement and use various architectures to solve problems in the area of image classification, language translation and NLP using PyTorch.

BookFeb 2018262 pages
Neural Network Projects with Python

Neural Network Projects with Python

This book contains practical implementations of several deep learning projects in multiple domains, including in regression-based tasks such as taxi fare prediction in New York City, image classification of cats and dogs using a convolutional neural network, implementing a facial recognition security system using Siamese Neural Networks, and more.

BookFeb 2019308 pages

Personalised recommendations for you

Based on your interests and search pattern

Et al.

Et al.

Ever wonder why speech recognition systems don't understand the Scottish accent, or what would happen if an astronaut only ate mac 'n' cheese, or other spurious reflections you'd have at a bar? We did, then collated those deliberations into absurd research articles with fake figures and methodologies inspired by even more fictionally absurd studies.

BookAug 2023230 pages5
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages4
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages5
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages1
Generative AI with LangChain

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages5
Mastering Tableau 2023

Mastering Tableau 2023

This book is a comprehensive resource to mastering your Tableau skills and becoming a BI expert. As you progress, you will learn how to build advanced dashboards and improve your storytelling to derive key business insight, as well as make you well-versed with advanced functionalities of Tableau in the business intelligence domain.

BookAug 2023684 pages
Building AI Applications with ChatGPT APIs

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

BookSep 2023258 pages5
Building AI Applications with ChatGPT APIs

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

BookSep 2023258 pages2
Data Engineering with AWS

Data Engineering with AWS

Embark on a journey to master data engineering pipelines on AWS! Our book offers a hands-on experience of AWS services for ingesting, transforming, and consuming data. Whether you're an absolute beginner or someone with basic data engineering experience, this guide is an indispensable resource.

BookOct 2023636 pages5
Modern Data Architecture on AWS

Modern Data Architecture on AWS

Every organization wants an agile, performant, and cost-effective data platform that meets all their current and future business needs. Purpose-built AWS analytics services and their features play a big part in building such a modern data platform. This book brings to you all the design and architectural patterns that’ll help you achieve this goal.

BookAug 2023420 pages5
Practical Guide to Applied Conformal Prediction in Python

Practical Guide to Applied Conformal Prediction in Python

Discover the power of Conformal Prediction with the "Practical Guide to Applied Conformal Prediction in Python." Master the latest techniques to quantify uncertainty in machine learning and computer vision models, and seamlessly apply them to your industry applications.

BookDec 2023240 pages
TinyML Cookbook

TinyML Cookbook

With over 70 project-based recipes, the TinyML Cookbook is a practical guide that will help you to get the most out of your microcontrollers. It provides a comprehensive understanding of the theoretical foundations while giving you hands-on experience training ML models for deployment on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano microcontrollers.

BookNov 2023664 pages