Course Code

OpenNN

Duration

14 hours (usually 2 days including breaks)

Requirements

An understanding of data science concepts
C++ programming experience is helpful

Audience

Software developers and programmers wishing to create Deep Learning applications.

Overview

In this instructor-led, live training, we go over the principles of neural networks and use OpenNN to implement a sample application.

Format of the course

Lecture and discussion coupled with hands-on exercises.

Course Outline

Introduction to OpenNN, Machine Learning and Deep Learning

Downloading OpenNN

Working with Neural Designer

Using Neural Designer for descriptive, diagnostic, predictive and prescriptive analytics

OpenNN architecture

CPU parallelization

OpenNN classes

Data set, neural network, loss index, training strategy, model selection, testing analysis
Vector and matrix templates

Building a neural network application

Choosing a suitable neural network
Formulating the variational problem (loss index)
Solving the reduced function optimization problem (training strategy)

Working with datasets

The data matrix (columns as variables and rows as instances)

Learning tasks

Function regression
Pattern recognition

Compiling with QT Creator

Integrating, testing and debugging your application

The future of neural networks and OpenNN

Summary and conclusion

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:Neural Networks Fundamentals using TensorFlow as Example

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

The trainer very easily explained difficult and advanced topics.

Leszek K

Course:Artificial Intelligence Overview

Machine Translated

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

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:Neural Network in R

Machine Translated

Examples on real data.

Alior Bank S.A.

Course:Neural Network in R

Machine Translated

neuralnet, pROC in a loop.

Alior Bank S.A.

Course:Neural Network in R

Machine Translated

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

ING Bank Śląski S.A.; Kamil Kurek Programowanie

Course:Understanding Deep Neural Networks

Machine Translated

Lack

ING Bank Śląski S.A.; Kamil Kurek Programowanie

Course:Understanding Deep Neural Networks

Machine Translated

Big theoretical and practical knowledge of the lecturers. Communicativeness of trainers. During the course, you could ask questions and get satisfactory answers.

Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie

Course:Understanding Deep Neural Networks

Machine Translated

Practical part, where we implemented algorithms. This allowed for a better understanding of the topic.

ING Bank Śląski S.A.; Kamil Kurek Programowanie

Course:Understanding Deep Neural Networks

Machine Translated

exercises and examples implemented on them

Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie

Course:Understanding Deep Neural Networks

Machine Translated

Examples and issues discussed.