Elias 's starred repositories
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
awesome-interview-questions
:octocat: A curated awesome list of lists of interview questions. Feel free to contribute! :mortar_board:
OpenBBTerminal
Investment Research for Everyone, Everywhere.
NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
awesome-quant
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
Kalman-and-Bayesian-Filters-in-Python
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 filters, and more. All exercises include solutions.
backtrader
Python Backtesting library for trading strategies
statsmodels
Statsmodels: statistical modeling and econometrics in Python
anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes
Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.
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.
ThinkStats2
Text and supporting code for Think Stats, 2nd Edition
stanford-cs-221-artificial-intelligence
VIP cheatsheets for Stanford's CS 221 Artificial Intelligence
statrethinking_winter2019
Statistical Rethinking course at MPI-EVA from Dec 2018 through Feb 2019
Hands-On-Machine-Learning-for-Algorithmic-Trading
Hands-On Machine Learning for Algorithmic Trading, published by Packt
QuantitativePrimer
An Interview Primer for Quantitative Finance
learn-julia-the-hard-way
Learn Julia the hard way!
thundergbm
ThunderGBM: Fast GBDTs and Random Forests on GPUs
English-to-IPA
Converts English text to IPA notation
summarizer
A Reddit bot that summarizes news articles written in Spanish or English. It uses a custom built algorithm to rank words and sentences.