Course Code

MicrosoftCognitiveToolkit

Duration

21 hours (usually 3 days including breaks)

Requirements

An understanding of deep learning concepts
Experience programming in Python, C#, C++, or Java
Applied mathematics up to linear algebra

Overview

Microsoft Cognitive Toolkit 2.x (previously CNTK) is an open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 5-10x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for image-related tasks.

In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such as data, speech, text, and images.

By the end of this training, participants will be able to:

Access CNTK as a library from within a Python, C#, or C++ program
Use CNTK as a standalone machine learning tool through its own model description language (BrainScript)
Use the CNTK model evaluation functionality from a Java program
Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs)
Scale computation capacity on CPUs, GPUs and multiple machines
Access massive datasets using existing programming languages and algorithms

Audience

Developers
Data scientists

Format of the course

Part lecture, part discussion, exercises and heavy hands-on practice

Note

If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.

Course Outline

To request a customized course outline for this training, please contact us.

Testimonials

★★★★★

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease

Course:Artificial Neural Networks, Machine Learning, Deep Thinking

Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.

Gudrun Bickelq

Course:Introduction to the use of neural networks

The interactive part, tailored to our specific needs.

Thomas Stocker

Course:Introduction to the use of neural networks

I really appreciated the crystal clear answers of Chris to our questions.

Léo Dubus

Course:Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple

I generally enjoyed the knowledgeable trainer.

Sridhar Voorakkara

Course:Neural Networks Fundamentals using TensorFlow as Example

I was amazed at the standard of this class - I would say that it was university standard.

David Relihan

Course:Neural Networks Fundamentals using TensorFlow as Example

Very good all round overview. Good background into why Tensorflow operates as it does.

Kieran Conboy

Course:Neural Networks Fundamentals using TensorFlow as Example

I liked the opportunities to ask questions and get more in depth explanations of the theory.

Sharon Ruane

Course:Neural Networks Fundamentals using TensorFlow as Example

I liked the new insights in deep machine learning.

Josip Arneric

Course:Neural Network in R

We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.

Tea Poklepovic

Course:Neural Network in R

I mostly enjoyed the graphs in R :))).

Faculty of Economics and Business Zagreb

Course:Neural Network in R

Very flexible.

Frank Ueltzhöffer

Course:Artificial Neural Networks, Machine Learning and Deep Thinking

I generally enjoyed the flexibility.

Werner Philipp

Course:Artificial Neural Networks, Machine Learning and Deep Thinking

Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for.

Commerzbank AG

Course:Neural Networks Fundamentals using TensorFlow as Example

I was benefit from topic selection. Style of training. Practice orientation.

Commerzbank AG

Course:Neural Networks Fundamentals using TensorFlow as Example

The informal exchanges we had during the lectures really helped me deepen my understanding of the subject

Explore

Course:Deep Reinforcement Learning with Python

The trainer was a professional in the subject field and related theory with application excellently

Fahad Malalla - Tatweer Petroleum

Course:Applied AI from Scratch in Python

The trainer very easily explained difficult and advanced topics.

Leszek K

Course:Artificial Intelligence Overview

Machine Translated

Communication with lecturers

文欣张

Course:Artificial Neural Networks, Machine Learning, Deep Thinking

Machine Translated

like it all

lisa xie

Course:Artificial Neural Networks, Machine Learning, Deep Thinking

Machine Translated

a lot of exercises that I can directly use in my work.

Alior Bank S.A.

Course:Sieci Neuronowe w R

Machine Translated

Examples on real data.

Alior Bank S.A.

Course:Sieci Neuronowe w R

Machine Translated

neuralnet, pROC in a loop.

Alior Bank S.A.

Course:Sieci Neuronowe w R

Machine Translated

A wide range of topics covered and substantial knowledge of the leaders.

ING Bank Śląski S.A.; Kamil Kurek Programowanie