Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
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Updated
Feb 2, 2024 - HTML
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
LibRec: A Leading Java Library for Recommender Systems, see
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
Fast, flexible and easy to use probabilistic modelling in Python.
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
Bayesian inference with probabilistic programming.
Sample code for the Model-Based Machine Learning book.
Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.
Probabilistic Machine Learning course lab @Units
This project has two parts. In part one, we use markov random field to denoise an image. In Part two, we use similar model for image segmentation.
A domain-specific probabilistic programming language for scalable Bayesian data cleaning
🌀 Stanford CS 228 - Probabilistic Graphical Models
A list of time-lasting classic books, which not only help you figure out how it works, but also grasp when it works and why it works in that way.
Checking D-separations and I-equivalence in Bayesian Networks.
causact: R package to accelerate computational Bayesian inference workflows in R through interactive visualization of models and their output.
High-performance reactive message-passing based Bayesian inference engine
Orgainzed Digital Intelligent Network (O.D.I.N)
Official Repository of "Contextual Graph Markov Model" (ICML 2018 - JMLR 2020)
assignments and group case studies from PGDMLAI course by upGrad & IIITB
Curated materials for different machine learning related summer schools
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