Our Events

This presentation considers the alternative construction of the design space based on experiment data and a grey-box model of the reactions. The models are subsequently used to optimize the production process by changing the process variables. In addition, the effect of uncertainty and variability of the parameters on the process performance is also examined with a Monte-Carlo simulation.

You will learn how to author scenarios for simulation on realistic road networks designed in RoadRunner. You can use this workflow to simulate autonomous driving with built-in agents as well as author and integrate custom agents designed in MATLAB, Simulink, or CARLA. The scenarios can be exported to OpenSCENARIO for simulation and analysis in external tools if desired.

In webinar will be discussing how to incorporate AI into your project by understanding and implementing the steps of the AI workflow. We will show various demos using Machine Learning and Deep Learning techniques and discuss how MATLAB can work with open-source tools for AI projects.

Learn how to use MATLAB for hyperspectral imaging and aerial lidar data processing for terrain classification and vegetation detection in agricultural applications.

Signal processing and biomedical applications are becoming increasingly complex and computationally intensive. With the increasing adoption of machine learning and deep learning techniques, powerful hardware like multicore CPUs, GPUs, and High-Performance Computing clusters/cloud are common. With Parallel Computing Toolbox™, MATLAB® helps you take advantage of your hardware to speed up your applications without having to rewrite code. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming.

The MathWorks Finance Conference 2022 brings together industry professionals to showcase MathWorks tools in real-world industry use cases and offers practitioner advice through live presentations, Q&A, interactive panel discussions, and in-depth demos.

Deep Learning and Machine Learning are powerful tools to build applications for signals and time-series data across a broad range of industries. These applications range from predictive maintenance and health monitoring to financial portfolio forecasting and advanced driver assistance systems.

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.