Machine learning basics for protein modeling and design

This video introduces the fundamental machine learning concepts that undergird the deep learning methods in protein structure and design. Topics include: loss function, optimization techniques, activation functions, normalization, learning rate, training and back propagation. The conceptual lecture is supplemented with a tensorflow playground demo discussion. This video also covers applications of regression and classification for modeling and prediction and common node and edge structures.
Protein Modeling and Design with PyRosetta and Machine Learning
• Protein Modeling and D...
Video from the Rosetta Commons RaMP Bootcamp (July 2023)
Instructor: Deniz Akpinaroglu (UCSF)
Credits:
Instructor: Deniz Akpinaroglu (UCSF)
RaMP Director and Rosetta Commons Director: Jeffrey Gray (JHU)
RaMP Program Administrator:Camille Mathis (JHU)
Rosetta Commons Instructional Designer: Ashley Vater (UC Davis)
Video Production: Elizabeth Bonilla (JHU)
Funding: Rosetta Commons, National Science Foundation, and Johns Hopkins University