resources
A growing collection of my go-to resources and papers I’ve enjoyed reading.
RNN & LSTM:
- CS231n Recurrent Neural Networks notes
- Introduction to recurrent neural networks by Jeremy Jordan
- Understanding LSTM Networks by Christopher Olah
- Were RNNs All We Needed?
CNN:
- MIT: Machine Learning 6.036, Lecture 8: Convolutional neural networks (Fall 2020) by Tamara Brodrick
Diffusion Models:
Papers:
For me, a good paper is one that is easy to read and understand, while also imparting knowledge or answering a question.
- Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications
- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- Deep Residual Learning for Image Recognition
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
- Can LLMs subtract numbers?