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algorithmic-trading-with-python

Source code for Algorithmic Trading with Python (2020) by Chris Conlan

antialiased-cnns

Antialiasing cnns to improve stability and accuracy. In ICML 2019.

attention-mechanisms

Implementations for a family of attention mechanisms, suitable for all kinds of natural language processing tasks and compatible with TensorFlow 2.0 and Keras.

auto_ks

Repository for "Fitting a Kalman Smoother to Data"

automl_survey

awesome-learning-with-label-noise

A curated list of resources for Learning with Noisy Labels

awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

awesome-tensorflow

TensorFlow - A curated list of dedicated resources http://tensorflow.org

baikal

A graph-based functional API for building complex scikit-learn pipelines.

bentoml

Model Serving Made Easy

black-box-quantile-kernels

Black Box Quantiles for Kernel Learning

boss

This repository provides the code for replicating the experiments in the paper "Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance"

calibration-framework

Python Framework to calibrate confidence estimates of classifiers like Neural Networks

candlestick_scraper

Scrape candlestick data from crypto exchanges and upload it to Kaggle.

causal_inference_python_code

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

cb_and_cc

compute-canada-goodies

Automation scripts for compute canada

concrete-autoencoders

cwae

Cramer-Wold AutoEncoder

cxr_adv

Repository for the paper "An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs"

dain

Deep Adaptive Input Normalization for Time Series Forecasting

datatune_2024_presentation

slidedeck for "Techniques in Time Series Analysis for Machine Learning Enthusiasts"

deepensemble

Keras ensembles, made easy.

deephermites

differentiable-particle-filters

Source code and data for the paper "Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors"

dnn-and-hdft

Small Data Challenge: Structural Analysis and Optimization of Convolutional Neural Networks with a Small Sample Size - UPMC (University Of Pittsburgh)

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

esrnn-gpu

PyTorch GPU implementation of the ES-RNN model for time series forecasting

extrakit-learn

scikit-learn inspired extensions for machine learning

financial-models-numerical-methods

Collection of notebooks about quantitative finance, with interactive python code.