Addressing Deep Learning Challenges
Deep learning is getting lots of attention lately and for good reason. It's achieving results that were not possible before. Though, getting started might not always be easy. MATLAB being an integrated framework allows you to accelerate building consumer and industrial applications while utilizing the capabilities of open-source frameworks like TensorFlow to train the deep learning networks.
Join us for a hands-on MATLAB workshop, in which you will explore and learn about deep learning workflow in MATLAB while working on problem of Speech command recognition and tackling key concepts and challenges such as
- Accelerating/Automating ground truth labeling for data
- Designing and Validating deep neural networks
- Training and tuning deep learning algorithms
Also, we will be talking about the interoperability with different frameworks and workflow for deploying your deep learning algorithms to embedded targets.
Integrating Digital Twin and AI for Smarter Engineering DecisionsAmit Doshi
schedule 6 months agoSold Out!
With the increasing popularity of AI, new frontiers are emerging in predictive maintenance and manufacturing decision science. However, there are many complexities associated with modeling plant assets, training predictive models for them, and deploying these models at scale for near real-time decision support. This talk will discuss these complexities in the context of building an example system.
First, you must have failure data to train a good model, but equipment failures can be expensive to introduce for the sake of building a data set! Instead, physical simulations can be used to create large, synthetic data sets to train a model with a variety of failure conditions.
These systems also involve high-frequency data from many sensors, reporting at different times. The data must be time-aligned to apply calculations, which makes it difficult to design a streaming architecture. These challenges can be addressed through a stream processing framework that incorporates time-windowing and manages out-of-order data with Apache Kafka. The sensor data must then be synchronized for further signal processing before being passed to a machine learning model.
As these architectures and software stacks mature in areas like manufacturing, it is increasingly important to enable engineers and domain experts in this workflow to build and deploy the machine learning models and work with system architects on the system integration. This talk also highlights the benefit of using apps and exposing the functionality through API layers to help make these systems more accessible and extensible across the workflow.
This session will focus on building a system to address these challenges using MATLAB, Simulink. We will start with a physical model of an engineering asset and walk through the process of developing and deploying a machine learning model for that asset as a scalable and reliable cloud service.
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