hi, i am neuralnets!

A deep dive into Linear Regression and the math behind it

a deep dive into transformers and a visual guide into how it works

a deep dive into optimizers and a journey through time

a deep dive into recurrent neural networks and how the math behind it works

discussion into how text preprocessing, regex, frequencies, and word embeddings work

discussion into how pos tagging, ner, sentiment analysis, and n-gram models work

a guide into how hidden markov models, text clustering, attention work

a guide into how embeddings, positional embeddings, tokenizer (especially bpe tokenizer) work

a guide into how lora, model distillation, gradient clipping and early stopping work

a guide into how attention works, in great detail

a guide into how deepseek r1 works under the hood

a guide into how quantization occurs in llms