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bayesian-methods-for-ml

People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine. When applied to deep learning, Bayesian methods allow you to compress your models a hundred folds, and automatically tune hyperparameters, saving your time and money. In six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can automate this workflow and how to speed it up using some advanced techniques. We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases be found with Bayesian methods.

excalibur

A web interface to extract tabular data from PDFs

hmms

Continuous-time Hidden Markov Model

intro-to-dl

Resources for "Introduction to Deep Learning" course.

lanl-earthquake-prediction

https://www.kaggle.com/c/LANL-Earthquake-Prediction

machine-learning

ML Blueprints for quick prototyping

machine-learning-competition-2021

mt_ml

mt_ub

MT Proccesing of data loggers

mtpy

Python toolbox for standard Magnetotelluric (MT) data analysis

ralfcam.github.io

sandbox_scripts

collection of possibly useful scripts

stajax

JAX GPU acceleration of statistical models

stockwell-transform-colab

A blueprint for computing s-transforms in Google Colab

trader_notes

Notebooks collection of trading utils

wqu_capstone

WQU MSc in Financial Engineering - Capstone Course - 24/03 690

yandex-big-data-engineering