Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Updated
Feb 2, 2024  HTML
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
It is my belief that you, the postgraduate students and jobseekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.
Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
A python tutorial on bayesian modeling techniques (PyMC3)
Bayesian inference with probabilistic programming.
Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop
PyMC educational resources
A Python package for Bayesian forecasting with objectoriented design and probabilistic models under the hood.
Gaussian processes in TensorFlow
Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.
Statistical package in Python based on Pandas
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
RStan, the R interface to Stan
Code for modelling estimated deaths and cases for COVID19.
General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python
How to do Bayesian statistical modelling using numpy and PyMC3
A collection of Bayesian data analysis recipes using PyMC3
Statistical Rethinking (2nd ed.) with NumPyro
Probabilistic programming via source rewriting
rstanarm R package for Bayesian applied regression modeling
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