- Zero to Hero
- course.fast.ai
- https://developers.google.com/machine-learning/crash-course
- https://www.kaggle.com/learn
- https://github.com/dair-ai/pytorch_notebooks
- https://gitlab.engr.illinois.edu/saurabhg/dlcv-fa23-mps
- https://playground.tensorflow.org/
- LLM Visualization
- https://github.com/mdnoorit/letslearnai/blob/master/awesome_machine_learning_visualizations.md
- https://microscope.openai.com/
- https://distill.pub/
- https://aclanthology.org/2021.emnlp-main.446.pdf
- https://arxiv.org/pdf/1506.01066.pdf
- https://arxiv.org/pdf/2206.04301.pdf
- https://lena-voita.github.io/posts/emnlp19_evolution.html
- https://transformer-circuits.pub/
- https://www.lesswrong.com/posts/TvrfY4c9eaGLeyDkE/induction-heads-illustrated
- https://www.bishopbook.com/
- Учебник ШАДа по машинному обучению
- https://www.deeplearningbook.org/
- https://d2l.ai/
- Andrej Karpathy. A Recipe for Training Neural Networks
- https://github.com/google-research/tuning_playbook
- https://education.yandex.ru/handbook/ml/article/tonkosti-obucheniya
- https://colab.research.google.com/github/fastai/fastbook/blob/master/04_mnist_basics.ipynb - good intro on a basic MLP for MNIST. Excercise: train an MLP on entire MNIST
- https://www.kaggle.com/competitions/commonlitreadabilityprize/ (language -> score)
- https://www.kaggle.com/c/denoising-dirty-documents (image -> image)