Jose Campos's repositories
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
sandbox_scripts
collection of possibly useful scripts
Stockwell-Transform-Colab
A blueprint for computing s-transforms in Google Colab
trader_notes
Notebooks collection of trading utils