📕 Elements of Statistical Learning:

  • testo in pdf: https://web.stanford.edu/~hastie/ElemStatLearn/download.html
  • playlist con le sessioni registrate: https://www.youtube.com/playlist?list=PL4fOYM4iG6elOwDkG_SdkIpPphe2wBCWI
  • Esercizi svolti in Python: https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/South%20African%20Heart%20Disease.ipynb

🖌️ CycleGAN:

  • Paper CycleGAN: https://arxiv.org/abs/1703.10593
  • Un’implementazione in Pytorch: https://github.com/yunjey/mnist-svhn-transfer

🐶 Reinforcement Learning:

  • Tutorial sul reinforcement learning di Pytorch: https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html
  • Sutton, Barto “Reinforcement Learning: An Introduction”: http://incompleteideas.net/book/the-book.html

🕸️ GNN:

  • DeepMind “Learning to Simulate Complex Physics with Graph Networks”, https://arxiv.org/pdf/2002.09405.pdf
  • Video introduzione GNN: https://www.youtube.com/watch?v=fOctJB4kVlM&t=343s
  • Introduzione “visiva” alle GNN: https://distill.pub/2021/gnn-intro/
  • Spettro degli autovalori di un grafo: 1) http://blog.shriphani.com/2015/04/06/the-smallest-eigenvalues-of-a-graph-laplacian/ 2) https://www.frontiersin.org/articles/10.3389/fncom.2013.00189/full 3) https://towardsdatascience.com/spectral-graph-convolution-explained-and-implemented-step-by-step-2e495b57f801
  • Hamilton, “Graph Representation Learning” https://www.cs.mcgill.ca/~wlh/grl_book/files/GRL_Book.pdf

👄 NLP

🤖 Transformer:

  • Google, “Attention is all you need” : https://arxiv.org/pdf/1706.03762.pdf
  • Un’ottima e sintetica spiegazione dei transformer: https://jalammar.github.io/illustrated-transformer/
  • Una spiegazion un po’ più lunga: Transformers Explained Visually. Part 1: https://towardsdatascience.com/transformers-explained-visually-part-2-how-it-works-step-by-step-b49fa4a64f34 Part 2: https://towardsdatascience.com/transformers-explained-visually-part-1-overview-of-functionality-95a6dd460452

📥 Word embedding:

  • https://jalammar.github.io/illustrated-word2vec/

🧠 Pytorch, machine learning e neural networks:

  • Come funziona Pytorch Autograd?: https://www.youtube.com/watch?app=desktop&v=MswxJw-8PvE
  • Andrew NG, “Machine Learning Yearning”, https://github.com/ajaymache/machine-learning-yearning
  • “Dive into Deep Learning”: https://d2l.ai/index.html
  • Goodfellow, “Deep Learning”: https://www.deeplearningbook.org/

📰 Dataset:

  • Dataset list: https://www.datasetlist.com/
  • Applicazioni in industria: https://github.com/AndreaPi/Open-industrial-datasets
  • Nasa: https://www.nasa.gov/intelligent-systems-division#turbofan
  • 31 Free datasets for your next data science project: https://www.interviewquery.com/blog-free-datasets/
  • Hugging face: https://huggingface.co/datasets
  • data.world https://data.world/
  • Cityscapes: https://www.cityscapes-dataset.com/
  • Kaggle: https://www.kaggle.com/datasets

🦄 Altro:

  • Make your blog using github: https://beautifuljekyll.com
  • Organizzare appunti annidati e molto di più: https://www.notion.so/
  • Una comodissima whiteboard gratuita online; editabile contemporaneamente da più utenti: https://app.conceptboard.com/
  • Metti da parte un articolo per più tardi: https://getpocket.com/fr/
  • Per imparare la statistica, Wasserman, “All of statistics”: https://egrcc.github.io/docs/math/all-of-statistics.pdf
  • Corso di probabilità del MIT: https://www.youtube.com/playlist?list=PLUl4u3cNGP60hI9ATjSFgLZpbNJ7myAg6
  • Generare immagini da prompt (docker di Stable Diffusion): https://github.com/pieroit/stable-diffusion-jupyterlab-docker
  • Biblioteca di e-book: https://z-lib.org/