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alphalens

Performance analysis of predictive (alpha) stock factors

augmented-neural-odes

Pytorch implementation of Augmented Neural ODEs :sunflower:

autogluon

AutoGluon: AutoML for Image, Text, and Tabular Data

awesome-implicit-representations

A curated list of resources on implicit neural representations.

awesome-latex-drawing

Drawing Bayesian networks, graphical models, and technical frameworks in LaTeX.

backtrader

Python Backtesting library for trading strategies

bayesian-neural-networks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace and more

bayesian-timeseries

Predicting market price of an agricultural product (arecanut) using Bayesian time series methods

bayesianspectralestimation

Bayesian nonparametric spectral estimation

bert

TensorFlow code and pre-trained models for BERT

bluematrix.github.io

btgym

Scalable, event-driven, deep-learning-friendly backtesting library

chess-alpha-zero

Chess reinforcement learning by AlphaGo Zero methods.

cn-dpm

Official code for ICLR 2020 paper "A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning."

combo

continual-learning

PyTorch implementation of various methods for continual learning (XdG, EWC, online EWC, SI, LwF, DGR, DGR+distill, RtF, iCaRL).

contrastivecrop

[CVPR 2022 Oral] Crafting Better Contrastive Views for Siamese Representation Learning

convnets-as-gps

Code for "Deep Convolutional Networks as shallow Gaussian Processes"

coopnets

Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching

cs231n.github.io

Public facing notes page

datacamp

DataCamp data-science courses

deep-kernel-transfer

Official pytorch implementation of the paper "Deep Kernel Transfer in Gaussian Processes for Few-shot Learning"

deep-probabilistic-koopman

Deep Probabilistic Koopman: long-term time-series forecasting under quasi-periodic uncertainty

deepcgp

Deep convolutional gaussian processes.

deeplob-deep-convolutional-neural-networks-for-limit-order-books

This jupyter notebook is used to demonstrate our recent work, "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books", published in IEEE Transactions on Singal Processing. We use FI-2010 dataset and present how model architecture is constructed here. The FI-2010 is publicly avilable and interested readers can check out their paper.

differential-dgp

Implementation of stochastic variational inference for differentially deep gaussian processes

diffusion_models

A series of tutorial notebooks on denoising diffusion probabilistic models in PyTorch

dirichlet-hawkes-process

disentanglement_lib

disentanglement_lib is an open-source library for research on learning disentangled representations.

dockerfile-ubuntu-gnome