Lists (19)
Starred repositories
50
stars
written in Jupyter Notebook
Clear filter
A game theoretic approach to explain the output of any machine learning model.
The fastai book, published as Jupyter Notebooks
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
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 filte…
A multi-voice TTS system trained with an emphasis on quality
FinRL®: Financial Reinforcement Learning. 🔥
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Python toolkit for quantitative finance
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
Automatic extraction of relevant features from time series:
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)
Collection of notebooks about quantitative finance, with interactive python code.
List of papers, code and experiments using deep learning for time series forecasting
An interactive book on deep learning. Much easy, so MXNet. Wow. [Straight Dope is growing up] ---> Much of this content has been incorporated into the new Dive into Deep Learning Book available at …
3D-printable hexagonal mirror array capable of reflecting sunlight into arbitrary patterns
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoe…
Python libraries for Google Colaboratory
Hands-On Machine Learning for Algorithmic Trading, published by Packt
fastquant — Backtest and optimize your ML trading strategies with only 3 lines of code!
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Measures the latency between CPU cores
Ipython notebooks for math and finance tutorials
Complete implementations from "Algorithms for Modern Hardware"
A knowledge graph and a set of tools for drug repurposing
Code accompanying my blog post: So, what is a physics-informed neural network?