Deep Learning and Reinforcement Learning Webinar Series For Automotive Applications

With just a few lines of MATLAB® code, you can apply deep learning techniques to your work whether you’re designing algorithms, preparing and labeling data, or generating code and deploying to embedded systems. This seminar series is designed to enable both engineers and data scientists make apply Artificial Intelligence concepts to your everyday work.

Here are a few highlights the MathWorks experts we will cover in this live webinar series:

  • Managing big data, automated labeling, and augmenting large data sets (images, signals, text, etc.)
  • Utilizing intuitive interfaces to easily create, visualize, analyze, and train networks or manage multiple experiments
  • Leveraging pre-trained models (e.g. GoogLeNet and ResNet) and imported models from Keras-TensorFlow, Caffe, and the ONNX Model format for transfer learning
  • Automating Ground Truth Image or Video Labeling
  • Training and Evaluating an Object Detector
  • Deploying implementation by generating optimized native embedded code
  • Applying Reinforcement Learning to typical Automotive applications and achieve the following:
    • Improve performance compared to traditional control algorithms (e.g. PID)
    • Create decision making strategies for systems where standard approaches are insufficient
    • Generate trained models to standalone, native C or CUDA code for embedded devices

Please use the registration links below and join us for one or all the free, live webinars.

Event Topic  
May 27th Intro to Deep Learning with MATLAB Register
June 3rd  Deep Learning for Images and Video Register
June 10th Reinforcement Learning: Leveraging Deep Learning for Controls Register

About the Presenters

Sharon Kim

Sharon Kim holds a M.S. and Ph.D. in Biomedical Engineering from Columbia University, and a B.S. in Biomedical Engineering from Johns Hopkins. As an Application Engineer at MathWorks in Santa Clara, she supports MATLAB users in image and signals processing, machine learning, deep learning, and more. Prior to joining MathWorks, she studied resting state brain activity patterns by applying analysis methods such as dimensionality reduction and unsupervised machine learning to wide-field optical image data.

Shyam Keshavmurthy

At MathWorks, Shyam enables engineers from the top Automotive OEM’s and Tier-1 suppliers apply MATLAB and Simulink to solve business critical Big Data challenges. He has a PhD in Nuclear and Radiological Engineering and a graduate from Harvard Business Analytics program, he has been practicing AI applications for over 15 years in areas of automotive manufacturing and robotics.