Max's repositories
Bayesian-Filtering
Bayesian Filtering & Smoothing demos
deep-Bayesian-nonparametrics-papers
The collection of papers about combining deep learning and Bayesian nonparametrics
Conference-Acceptance-Rate
Acceptance rates for the major AI conferences
continual_learning_papers
Relevant papers in Continual Learning
deep-learning-wizard
Open source guides/codes for mastering deep learning to deploying deep learning in production in PyTorch, Python, C++ and more.
dlwpt-code
Code for the book Deep Learning with PyTorch by Eli Stevens and Luca Antiga.
free-spoken-digit-dataset
A free audio dataset of spoken digits. Think MNIST for audio.
Gaussian-Process-textbook-examples
Some demos about GPs
GNNPapers
Must-read papers on graph neural networks (GNN)
GPSSMtorch
An implementation of GP-SSM inference algorithms using pytorch
Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Machine-Learning-algorithms-Matlab
Contains ML Algorithms implemented as part of CSE 512 - Machine Learning class taken by Fransico Orabona. Implemented Linear Regression using polynomial basis functions, Perceptron, Ridge Regression, SVM Primal, Kernel Ridge Regression, Kernel SVM, Kmeans.
Machine.Learning.Models.pytorch
Machine Learning models in general in PyTorch.
mlbop
Matlab code for S. Theodoridis' "Machine Learning: A Bayesian and Optimization Perspective" (2015).
random-fourier-features
Code for random Fourier features based on Rahimi and Recht's 2007 paper.
SSGPR
Sparse Spectrum Gaussian Process Regression
TGP.pytorch
Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021
Visualization-of-popular-algorithms-in-Python
Visualization of popular algorithms using NetworkX Graph libray