Pinned Repositories
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"
interval_load_forecasting
24-hour ahead interval load data forecasting
machine-learning-interview-enlightener
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
mirkwood
ParetoMTL
Code for Neural Information Processing Systems (NeurIPS) 2019 paper: Pareto Multi-Task Learning
stellar_denoising
tsbootstrap
tsbootstrap: generate bootstrapped time series samples in Python
astrogilda's Repositories
astrogilda/tsbootstrap
tsbootstrap: generate bootstrapped time series samples in Python
astrogilda/mirkwood
astrogilda/interval_load_forecasting
24-hour ahead interval load data forecasting
astrogilda/machine-learning-interview-enlightener
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
astrogilda/awesome-tensorflow
TensorFlow - A curated list of dedicated resources http://tensorflow.org
astrogilda/BentoML
Model Serving Made Easy
astrogilda/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"
astrogilda/cb_and_cc
astrogilda/datatune_2024_presentation
slidedeck for "Techniques in Time Series Analysis for Machine Learning Enthusiasts"
astrogilda/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.
astrogilda/great_expectations
Always know what to expect from your data.
astrogilda/machine-learning-for-trading
Notebooks, resources, and references accompanying the book Machine Learning for Algorithmic Trading
astrogilda/manifold_mixup
Tensorflow implementation of the Manifold Mixup machine learning research paper
astrogilda/MKLpy
A package for Multiple Kernel Learning in Python
astrogilda/mlfinlab
MlFinlab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
astrogilda/openTSNE
Extensible, parallel implementations of t-SNE
astrogilda/optiver-trading-close
astrogilda/python-for-hpc
Python for HPC: Community Materials
astrogilda/ray
A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
astrogilda/resume
astrogilda/sktime
A unified framework for machine learning with time series
astrogilda/STRIPE
Code for our NeurIPS 2020 paper "Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity"
astrogilda/tensorpack
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
astrogilda/tf-TabNet
A Tensorflow 2.0 implementation of TabNet.
astrogilda/TransCal
astrogilda/tspfnbeats
astrogilda/uncertainty-baselines
High-quality implementations of standard and SOTA methods on a variety of tasks.
astrogilda/uncertainty-toolbox
A python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
astrogilda/Unsupervised-Domain-Adaptation-For-Star-Formation-History
Unsupervised domain adaptation methods applied to the prediction of star formation history using simulated data
astrogilda/yogadl
yogadl, the flexible data layer