Title: Demystifying Deep Learning
Dates: July 20-21
Time: 9:30 AM-4:30 PM
Location: Virtual via Zoom
Available Seats: All seats have been filled at this time.
Instructor: Dr. Antonio Moretti, Faculty Fellow, Computer Science Department, Barnard College - Website
Description: This two-day virtual Zoom workshop provides a comprehensive introduction to deep learning for beginners. Some exposure to Python, multivariate calculus, and linear algebra would be helpful. The first half of the workshop will focus on the theory of deep learning including forward and backpropagation, convolutional neural networks, sequence models, transformers, and deep generative models. The second half will serve as a tutorial on automatic differentiation where we use the Python libraries Tensorflow and Pytorch to train and deploy a model on the cloud. All disciplines are welcome. Participants will be able to train and deploy a deep learning model by the workshop's end.
Deep learning refers to a family of models based on artificial neural networks that have recently produced breakthroughs in our ability to analyze images, videos, sound and text. Facial recognition, voice search, medical imaging and self-driving cars are all examples of technologies that are powered by deep learning. This workshop will provide a clear and concise introduction to the theory and practice of deep learning including forward and backpropagation, convolutional neural networks, sequence models, transformers, and deep generative models. A tutorial on automatic differentiation will also be covered where we use the Python libraries Tensorflow and Pytorch to train and deploy a model on the cloud.
- Forward and backpropagation
- Convolutional neural networks
- Sequence models (RNNs and LSTMs)
- Deep generative models
- Automatic differentiation
- Tensorflow and/or Pytorch- Model Deployment
Hosted by the NDSA Central Hub - Clark Atlanta University