This post will contain resources I have used and found helpful in my learning journey of PyTorch and Deep Learning. I will update this post as I discover more resources.
PyTorch Videos
- PyTorch for Deep Learning & Machine Learning : Beginner PyTorch course taught by Daniel Bourke. It covers the basics of PyTorch, neural networks, and applications in computer vision. Code-first approach with hands-on examples in Google Colab and course textbook, Learn PyTorch for Deep Learning: Zero to Mastery book.
Deep Learning Research
Cardiology
- EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation - A research paper on a vision-language model for echocardiography interpretation. The code is implemented in PyTorch and available on GitHub.
Opthalmology
Deep learning applications in opthamology - Talk by Aaron Y. Lee that covers the beginnings of the application of deep learning in the field of ophthalmology and vision science.
Transforming Healthcare with AI: Lessons from Ophthalmology - Talk by Pearse Keane on how AI is transforming healthcare, with a focus on ophthalmology.
Electronic Health Record (EHR)
- InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records - A research paper on using deep geometric learning to analyze electronic health records for clinical phenotype resolution. The code is implemented in PyTorch and available on GitHub.