Our Events

MATLAB® and Simulink® with deep learning frameworks, TensorFlow and PyTorch, provide enhanced capabilities for building and training your machine learning models. Via interoperability, you can take full advantage of the MATLAB ecosystem and integrate it with resources developed by the open-source community. You can combine workflows that include data-centric preprocessing, model tuning, model compression, model integration, and automatic code generation with models developed outside of MATLAB.

This workshop will give attendees a brief background on MATLAB’s Neural Network capabilities and introduce the ways that MATLAB makes designing, training, and implementing neural networks systems easier.Learn how to do deep learning in 6 lines of code, perform transfer learning using a GUI-based app and accelerate your code using MATLAB.

Artificial Intelligence (AI) is bringing a drastic change in technological fields, where it can be implemented to automate the system for more efficiency and performance. AI is now getting used in multiple fields from simply your mobile phone to diagnosing the diseases providing a high-performance and accurate system work with efficiency. In this webinar, you will see the workflow of artificial intelligence that can be implement in the industry. The workflow spans from data preparation to the deployment of the algorithm in real life application.

Explore how to increase productivity using Apps for design and analysis. Deep Neural Networks are becoming a ubiquitous tool for all industries particularly the computer vision applications of Machine Learning. Learn how Deep Learning is used in industries such as biomedicine, industrial automation.

In this webinar, we will demonstrate a real-world medical application of an ECG analysis and show how you can use MATLAB & Simulink to train and verify AI models. You will learn some basic and advanced techniques in order to fully utilize the benefits of machine learning in biomedical signal analysis.

MATLAB® and Simulink® with deep learning frameworks, TensorFlow and PyTorch, provide enhanced capabilities for building and training your machine learning models. Via interoperability, you can take full advantage of the MATLAB ecosystem and integrate it with resources developed by the open-source community. You can combine workflows that include data-centric preprocessing, model tuning, model compression, model integration, and automatic code generation with models developed outside of MATLAB.

MATLAB provides flexible, two-way integration with many programming languages, including Python. This allows different teams to work together and use MATLAB algorithms within production software and IT systems. This webinar will cover how to call MATLAB from Python and how to call Python libraries from MATLAB.

Did you know that MathWorks has thousands of developers working across all our products? And that we release regular maintenance packages as well as two major releases per year? Drop into one of these sessions to see some of the product highlights that have been added into MATLAB and toolboxes recently. But more than just highlighting a list of features, we’ll share some tips and demos to get the best use out of these as well.

Are you working with engineering or scientific data? Trying to develop an algorithm or model, create a report, or just gain some insights? Join us for this session where we will show how to use the high-level MATLAB language and interactive tools to solve an example problem.