Main Page > Demystifying Deep Learning: A practical approach in MATLAB

How much do you know about MATLAB for Artificial Intelligence?

Deep learning, a chief driver of the AI revolution, can achieve state-of-the-art accuracy in many cognitive or perceptual tasks such as naming objects in a scene or recognizing optimal paths in an environment.

It involves assembling large data sets, creating a neural network, and training, visualizing, and evaluating different models, using specialized hardware – often requiring unique programming knowledge. These tasks are frequently even more challenging because of the complex theory behind them.

Join us for a seminar about new MATLAB features that simplify these tasks and eliminate the low-level programming. Leave the room with an insight about current trends in engineering education.

Demystifying deep learning: A practical approach in MATLAB
Loren Shure, The MathWorks

Current Trends in Engineering Education
Alex Tarchini, The MathWorks

Monday, 23rd March 2020
14:00-16:30

Location: Technical University of Cluj Napoca,
Auditorium “Alexandru Domșa” – Constantin Daicoviciu Str. 15.

In this seminar Loren will show you how you can build and train neural networks that:

  • recognize handwriting,
  • categorize foods,
  • classify signals,
  • and control machines.
 
Admission is free but seats are limited.

Presenters

Loren Shure has worked at MathWorks for over 30 years. For the first 27 of these years, Loren co-authored several MathWorks products in addition to adding core functionality to MATLAB, including major contributions to the design of the MATLAB language. She is currently part of the Application Engineering team, enabling Loren to spend more time and energy working with customers.

Alessandro Tarchini has worked since 1993 in favor of advanced numerical analysis techniques facilitating the adoption of MATLAB in Italy. After managing MathWorks Italian operations for 10 years, Alex moved to Educational Business and Market Development and is now helping academic organization to maximize the value of MATLAB in teaching and research.

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