BERT_Links
https://www.youtube.com/channel/UCmct-3iP5w66oZzN_V5dAMg/playlists https://careers.sig.eu/o/machine-learning-software-engineering-researcher
Credit Cards: The Business of Enslaving Poor People https://www.youtube.com/watch?v=bua07BbeJC0
https://distribution-explorer.github.io/ https://www.kaggle.com/rhtsingh/code https://github.com/ashishpatel26/Class-Imbalance https://github.com/ashishpatel26/best-of-ml-python https://github.com/ml-tooling/best-of-ml-python https://towardsai.net/p/l/kernels-vs-filters-demystified?utm_source=twitter&utm_medium=social&utm_campaign=rop-content-recycle https://medium.com/mlearning-ai/how-normalization-affects-image-styles-edec31841bb4 https://neptune.ai/blog/xgboost-vs-lightgbm?utm_source=linkedin&utm_medium=post&utm_campaign=blog-xgboost-vs-lightgbm https://www.youtube.com/c/PythonSimplified/videos https://github.com/StatMixedML?tab=repositories https://www.youtube.com/playlist?list=PLoyGOS2WIonajhAVqKUgEMNmeq3nEeM51 https://dialog-systems-class.github.io/readings.html#now https://sites.google.com/view/sandeepkr/teaching/ell-888-advanced-machine-learning https://www.ipb.uni-bonn.de/teaching/ https://gitsearcher.com/ https://allendowney.github.io/DSIRP/
https://minitorch.github.io/ https://github.com/minitorch/minitorch
https://github.com/ashishpatel26/Treasure-of-Transformers https://nlp-css-201-tutorials.github.io/nlp-css-201-tutorials/
Deep learning for all at Manas lab IIT Mandi
https://gmihaila.github.io/ https://www.youtube.com/channel/UCQlfzrQCOF7hYnw6cqekr1A/playlists https://www.youtube.com/channel/UC1Ydk1dFuC3In6zWUaBXFZQ/videos https://web.stanford.edu/class/cs224n/materials/CS224N_PyTorch_Tutorial.html https://towardsdatascience.com/understanding-dimensions-in-pytorch-6edf9972d3be https://hal.archives-ouvertes.fr/hal-03038776/document https://compneuro.neuromatch.io/tutorials/intro.html https://engineering.purdue.edu/DeepLearn/pdf-kak/ https://androidkt.com/use-the-batchnorm-layer-in-pytorch/ https://twitter.com/abhi1thakur/status/1470406419786698761
https://docs.wandb.ai/ref/app/features/panels/weave/embedding-projector
http://web.stanford.edu/class/cs124/
https://www.eecs.yorku.ca/~kosta/Courses/EECS4422-F21/index.html
https://franknielsen.github.io/GSI/
https://nlp.stanford.edu/~johnhew/vocab-expansion.html
https://www.youtube.com/watch?v=-NBvRNPRzTo
https://stackoverflow.com/questions/42883547/intuitive-understanding-of-1d-2d-and-3d-convolutions-in-convolutional-neural-n https://stackoverflow.com/questions/37095783/how-is-a-convolution-calculated-on-an-image-with-three-rgb-channels?noredirect=1&lq=1 https://stackoverflow.com/questions/54727606/how-do-convolutional-layers-cnns-work-in-keras?noredirect=1&lq=1 https://stackoverflow.com/questions/65275805/how-to-implement-two-layers-of-keras-conv1d-in-numpy?rq=1
https://kazemnejad.com/blog/transformer_architecture_positional_encoding/
https://jalammar.github.io/illustrated-transformer/ https://e2eml.school/transformers.html https://nlp.seas.harvard.edu/2018/04/03/attention.html
https://colah.github.io/posts/2015-09-Visual-Information/ https://www.countbayesie.com/blog/2017/5/9/kullback-leibler-divergence-explained
๐๐ก๐ ๐ง๐๐ฐ ๐๐๐ข๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐๐ฅ ๐๐๐ญ๐ก๐ข๐ง๐ค๐ข๐ง๐ ๐ฌ๐ญ๐๐ซ๐ญ๐๐! โค๏ธโค๏ธโค๏ธ
If you are looking for a resource for learning Bayesian statistics, I highly recommend checking the Statistical Rethinking course by Prof. Richard McElreath. I consider both the book and the course as one of the best resources for learning Bayesian statistics - pure joy ๐.
The first lecture is available now on YouTube ๐ฅ: https://lnkd.in/gkzgu9U2
๐๐ก๐๐ญ? Statistical Rethinking focuses on Bayesian methods and approaches for making inferences from data with examples in #R and STAN. Code in other languages available as well and includes another flavor of R with Tidyverse + ggplot2 + brms, Python with PyMC3, and Julia with Turing (see links below๐๐ผ). The book and the course go hand in hand cover the foundations of Bayesian statistics, and it includes the following topics:
- Counting and probability (probably the best explanation I have heard)
- Sampling
- Linear regression and parameter estimation
- Splines
- Markov Chain Monte Carlo
- Binomial and Poisson regression
- Generalized Linear Model
๐๐จ๐ฎ๐ซ๐ฌ๐ ๐ซ๐๐ฌ๐จ๐ฎ๐ซ๐๐๐ฌ ๐๐จ๐ฎ๐ซ๐ฌ๐ ๐๐ข๐ญ๐ก๐ฎ๐: https://lnkd.in/giT2TsDJ ๐๐ข๐๐๐จ ๐ฅ๐๐๐ญ๐ฎ๐ซ๐๐ฌ (2019 ๐ฏ๐๐ซ๐ฌ๐ข๐จ๐ง): https://lnkd.in/gbi-RiE3 ๐๐จ๐จ๐ค ๐ฆ๐๐ข๐ง ๐๐จ๐๐ (๐ ๐ฏ๐๐ซ๐ฌ๐ข๐จ๐ง): https://lnkd.in/gizQsa5z ๐๐ข๐๐๐ฏ๐๐ซ๐ฌ๐ ๐ฏ๐๐ซ๐ฌ๐ข๐จ๐ง: https://lnkd.in/gXmpKPHt ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ฏ๐๐ซ๐ฌ๐ข๐จ๐ง: https://lnkd.in/gYvkwvPe ๐๐ฎ๐ฅ๐ข๐ ๐ฏ๐๐ซ๐ฌ๐ข๐จ๐ง๐ฌ: https://lnkd.in/g9e__-7m