References
The book 'Decoding GPT' introduces you to the basics of large language models. After reading it, some of you might feel motivated to delve deeper into the topic. I have given some references for these readers in the ‘Further Reading’ section at the end of the book.
However, the field of GenAI and LLM is a dynamic one. One time references in a book cannot hope to cover the new developments in the area. I have added this section to the website to cater to current updates. Here, I will maintain an ever updating list of books, articles, papers and videos that an advanced learner of LLMs will find useful.
01
The Machine Learning Mastery blog will help you for a wide variety of topics in machine learning, neural networks and transformers. https://machinelearningmastery.com/
02
As an example, see this article on transformers:https://machinelearningmastery.com/the-transformer-model/
03
Jay Alammar is an expert in LLMs and GenAI. He writes and creates videos about transformers and many other related subjects. You will find many useful articles on his blog: https://jalammar.github.io/ Here is an written by him article on transformers: https://jalammar.github.io/illustrated-transformer/
04
For those of you who like to code, here is an article that explains the ‘Attention Is All You Need’ paper through writing code: https://nlp.seas.harvard.edu/annotated-transformer/