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