projects

1. http://neuralnetworksanddeeplearning.com/chap2.html Michael Nielsen
2. https://stanford.edu/~jlmcc/papers/PDP/Volume 1/Chap8_PDP86.pdf Chapter 11 (book:jlmcc)
3. http://www.cs.utoronto.ca/~hinton/absps/naturebp.pdf 1986 paper
4. https://cs231n.github.io/optimization-2/ CS231n
5. https://d2l.ai/chapter_recurrent-neural-networks/bptt.html Rnn
6. https://www.youtube.com/watch?si=hAIpFPMYC79dxNwS&v=VMj-3S1tku0&feature=youtu.be Micrograd
7. https://www.pycodemates.com/2023/08/backpropagation-through-time-explained-with-derivations.html BPTT
Title Column 1 Column 2 Column 3
1. https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
2. https://github.com/karpathy/char-rnn?tab=readme-ov-file Char model
(karpathy)
http://karpathy.github.io/2015/05/21/rnn-effectiveness/ (Karpathy)
3. https://d2l.ai/chapter_recurrent-neural-networks/index.html D2L RNN✅
4. https://cs231n.github.io/rnn/ CS231n
5. https://www.bioinf.jku.at/publications/older/2604.pdf LSTM paper
https://www.cs.utoronto.ca/~ilya/pubs/2011/LANG-RNN.pdf RNN paper
6. https://www.fit.vut.cz/person/imikolov/public/rnnlm/thesis.pdf Thesis
Book
https://web.stanford.edu/~jurafsky/slp3/8.pdf Book Ch8✅

nlp

OpenCV

projects

blogs

RNN

Transformers

gemini

https://genai-handbook.github.io/

Pytorch

VAE-GAN