Python code for "Probabilistic Machine learning" book by Kevin Murphy

Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)

Notebooks and various random fun

Methods to get the probability of a changepoint in a time series.

🔥 A collection of PyTorch notebooks for learning and practicing deep learning

Repository for tutorial sessions at EEML2020

Practical notebooks for Khipu 2019, held in Universidad de la República in Montevideo.

Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)

A 1D analogue of the MNIST dataset for measuring spatial biases and answering Science of Deep Learning questions.

Understanding ML and deep learning through geometry

Implemented Machine Learning Algorithms in Hyperbolic Geometry (MDS, K-Means, Support vector machines, etc.)

Papers and code related to 'Less Than One'-Shot (LO-Shot) Learning

Code implementation of paper Towards A Deep and Unified Understanding of Deep Neural Models in NLP

Experiments on different dataset on how to grow networks during training to learn new image categories.

Code for "Language GANs Falling Short"

Practical sessions for the probabilistic graphical models class of AMMI.