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leetcode-go

✅ Solutions to LeetCode by Go, 100% test coverage, runtime beats 100% / LeetCode 题解

lottery

machine-learning-tutorials

machine learning and deep learning tutorials, articles and other resources

meta

META is a program for the meta analysis of genome-wide association studies. The program is designed to synthesizing the evidence from different association studies.

missing_data

Imputing missing stock anomalies data with EM implementation

mit-deep-learning-book-pdf

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

olic

On Line Inference for multiple Changepoints problem in time series model.

optimal-buy-cbpro

Scheduled buying of BTC, ETH, and LTC from Coinbase Pro, optimally!

pairs

paper-implementations

Use PyTorch to implement some classic frameworks

prmlt

Matlab code for algorithms in PRML book

pyenv-installer

This tool is used to install `pyenv` and friends.

pyrmt

Python library for Random Matrix Theory, cleaning schemes for correlation matrices, and portfolio optimization

quzzle

an interesting game similar to Hua

ranger

A Fast Implementation of Random Forests

rbaa

Regime Based Asset Allocation with MPT, Random Forest and Bayesian Inference

rbm_based_autoencoders_with_tensorflow

reprtree

Representative trees from ensembles

rocalphago

An independent, student-led replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search" (Nature 529, 484-489, 28 Jan 2016), details of which can be found on their website https://deepmind.com/publications.html.

rqpen

:exclamation: This is a read-only mirror of the CRAN R package repository. rqPen — Penalized Quantile Regression

rulefit

Python implementation of the rulefit algorithm

seqgan

Implementation of Sequence Generative Adversarial Nets with Policy Gradient

skranger

scikit-learn compatible Python bindings for ranger C++ random forest library

srnn

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

thetagang

ThetaGang is an IBKR bot for collecting money

tsibble

Tidy Temporal Data Frames and Tools

ultrafinance

Python project for real-time financial data collection, analyzing && backtesting trading strategies

xyz2

The xyz algorithm for fast interaction search in high-dimensional data.