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.

As Deep Learning becomes more prevalent across industries, there is a growing need to make it broadly available, accessible, and applicable – not just for data scientists but to engineers and scientists with varying specializations. 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. Some key aspects/challenges in building artificial intelligence (AI) applications include:

• Curating labeled datasets for supervised learning, including data augmentation and generation
• Applying traditional signal and image processing techniques to assist deep learning, and
• Integrating models with embedded or enterprise systems.

MATLAB is well-known for its strength in traditional engineering and scientific applications like image and signal processing, controls, and wireless system design. This talk demonstrates how you can work with various deep learning frameworks to make it easier to develop, deploy, and maintain AI-powered applications for many industrial use cases. Learn how you can:

• Automate ground truth labeling for image, video, Lidar and sensor data
• Apply physical models and simulations to augment training data, develop control algorithms and test the integrated system, potentially with hardware in the loop
• Generate high-performance C++ and CUDA engines for embedded system and cloud deployment

Also you accomplish this by interoperating with other deep learning frameworks at different points of your workflow:

• Data interoperability (like Parquet format) lets you preprocess signals or automatically label data in one framework while training models in another
• Model exchange formats (like ONNX) or importers let you evaluate and optimize a model trained in a different framework
• Compilation and automated code generation makes it easy to integrate models in cloud environments and with embedded or enterprise systems.