Popular GitHub Repositories for Probability Distributions

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

Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.

A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2025 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

ROADMAP(Mind Map) and KEYWORD for students those who have interest in learning NLP

The Machine Learning & Deep Learning Compendium was a list of references in my private & single document, which I curated in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.

The basic distribution probability Tutorial for Deep Learning Researchers

the open-source virtual assistant for Ubuntu based Linux distributions

Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI

A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

Lecture notes on Bayesian deep learning

collections of data science, machine learning and data visualization projects with pandas, sklearn, matplotlib, tensorflow2, Keras, various ML algorithms like random forest classifier, boosting, etc

List of DL topics and resources essential for cracking interviews

Normalizing flows in PyTorch

Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].

Resources I used for ML Engineer, Applied Scientist and Quant Researcher interviews.

?Language Model based sentences scoring library

Statistical Rethinking (2nd Ed) with Tensorflow Probability

A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

A project to deploy an online app that predicts the win probability for each NBA game every day. Demonstrates end-to-end Machine Learning deployment.