Our Events

This session demonstrates an end-to-end MATLAB workflow for developing anomaly detection models in the context of a pill production quality control data set comprising a large collection of images. The objective is to verify the quality of pills using automated visual inspection techniques.

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.

This series will look at two current trends affecting mining companies: the need to improve energy efficiency in order to reduce carbon emissions, and the growing benefits of autonomous mine operations. You will hear from experts in the field and MathWorks Australia engineers about the innovations and practical frameworks you can use to address these trends in your own operations.

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.