MATLAB for AI

Start Time	End Time
27 Aug 2024, 22:00 EDT	27 Aug 2024, 23:00 EDT

Overview

The field of AI is huge but for practical purposes this webinar will be split into two components: Machine Learning and Deep Learning. Differentiating between the two is based on a rule of thumb that Machine Learning is more numbers driven, whereas Deep Learning is vision focused. However, both Machine and Deep Learning workflows are conceptually similar so we will initially cover the overlapping workflow from preparation through to implementation and deployment.

Following that, we will dive into Low and No Code workflows for Machine Learning. This is the benchmark task where we have numerical data arranged in row/observation format that we wish to undertake regression or classification tasks on. The Low and No Code workflows in MATLAB allow the democratization of Machine Learning methods in that you don’t have to be a programming guru to get started. In fact, how long do you think it takes to build a model for deployment – seconds, minutes, hours or even days? It’s actually about a minute!

Next, the webinar will move to practical deep learning methods where we draw a distinction between the state of the art and what is used in practice. For instance, we have learnt with our customers that the extra few decimal places in accuracy for the latest and shiniest model comes with significant computational penalties, so there is a trade-off to be made. Interestingly in a controlled industrial environment, SqueezeNet and AlexNet work extremely well for almost zero computational cost

Highlights

Learn the AI common workflow
See No Code Machine Learning in practice
Observe the value of retraining a Deep Network

About the Presenter

Dr Emmanuel Blanchard is a senior application engineer at MathWorks who first joined the company as a training engineer in 2014. He focuses on data analytics. Prior to joining MathWorks, he was a Lecturer in Mechatronic Engineering at the University of Wollongong. He holds a PhD in Mechanical Engineering from Virginia Tech. He also worked as a Systems / Controls Engineer at Cummins Engine Company and as a research assistant in several research institutions in California and Virginia.

Dr Peter Brady is a Principal Application Engineer at the MathWorks where he leverages maths to accelerate our customers projects. He works across the engineering spectrum with a focus on maths, statistics, optimisation, machine and deep learning as well as cloud scale out. Prior to joining MathWorks Peter worked for several civil and defence contractors delivering projects in surface water, hydraulics and hydrology as well as fluid dynamics and submarine cavitation inception. Peter has a PhD in Mechanical Engineering and a Bachelors in Civil Engineering, both from UTS, and is a Chartered Practicing Engineer with Engineers Australia (CPEng NER) and a Certified Professional with the Australian Computer Society (MACS CP).

Product Focus

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27 Aug 2024, 22:00 EDT

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