My Machine Learning Blogs

• week 1 - linear regression
  A deep dive into Linear Regression and the math behind it
• week 2 - transformers
  a deep dive into transformers and a visual guide into how it works
• week 3 - optimizers
  a deep dive into optimizers and a journey through time
• week 4 - rnn
  a deep dive into recurrent neural networks and how the math behind it works
• week 5 - basics of nlp [part 1]
  discussion into how text preprocessing, regex, frequencies, and word embeddings work
• week 6 - basics of nlp [part 2]
  discussion into how pos tagging, ner, sentiment analysis, and n-gram models work
• week 7 - basics of nlp [part 3]
  a guide into how hidden markov models, text clustering, attention work
• week 8 - llms [part 1]
  a guide into how embeddings, positional embeddings, tokenizer (especially bpe tokenizer) work
• week 9 - enhance your model [part 1]
  a guide into how lora, model distillation, gradient clipping and early stopping work
• week 10 - llms [part 2]
  a guide into how attention works, in great detail
• deepseek r1 explanation
  a guide into how deepseek r1 works under the hood
• all about quantization
  a guide into how quantization occurs in llms