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: Transformer language models

Letcure 6: Vision and Audio Transformers

Letcure 7: Covers the basics of training and optimizing neural networks

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

Letcure 9: Prompt and Prefix Tuning