Hello :) Today is Day 65!
A quick summary of today:
- Lecture 16: Multimodal deep learning
- Lecture 17: Model analysis and explanation
- (just watched) Lecture 18 and 19: Future of NLP and Model Interpretability & Editing
First I will share my notes, and then just a quick summary and thoughts on the course.
Lecture 16: Multimodal deep learning
I got a lot of research papers to read from this lecture. Two are: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale; and Learning Transferable Visual Models From Natural Language Supervision.
Lecture 17: Model analysis and explanation
Quick summary and thoughts on the course
I wrote a lot of notes, and I am glad - writing something down with my hand definitely helps me remember it better.
Definitely a very comprehensive course and high quality, and I am glad I decided to commit to doing it. Going back to the start of NLP, through human languages > word vectors > word2vec > seq2seq > RNNs > LSTMs > Transformers, but also learning code generation, natural language generation, how LLMs word - pretraining, finetuning, evaluation metrics. Amazing.
Maybe someday I might pay for the XCS224N if I have the money, so that I can see lectures about current advances in the field. But this course (half of which was from 2021, half from 2023) definitely helped me create a sturd base for my future NLP journey. Thank you to Professor Chris Manning and head TA John Hewitt - they were top-notch ^^
That is all for today!
See you tomorrow :)