This post provides an overview of the content of this knowledge base.

Lecture 1: An Introduction to Deep Learning

Lecture 2: Covers Convolutional Neural Networks

Lecture 3: Covers language models before transformers: RNNs, Seq2Seq, LSTM models

Letcure 4: Covers transformer architecture in general and begins discussion on transformer language models

Letcure 5: Continues discussion on transformer language models

Letcure 6: Covers Vision Transformers

Letcure 7: Covers the basics of optimizing neural networks for classification

Letcure 8: Covers Contrastive Learning: Contrastive loss functions and associated model architectures.

Letcure 9: Prompt and Prefix Tuning