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A step-by-step guide to get started with Applied Machine Learning
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
Black Scholes and Merton option, greeks and implied volatility calc for PHP Laravel or Symfony package
Final Project for Denison's MATH 242 - Applied Statistic: A data analysis project to predict the likelihood of hitting a homerun in baseball based on batting statistics
Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.
Original source code for Beginning Java Objects 3rd Ed
A UI-friendly program calculating Black-Scholes options pricing with advanced algorithms incorporating option Greeks, IV, Heston model, etc. Reads input from users, files, databases, and real-time, external market feeds (e.g. APIs).
Work in progress transmit from Google Code
Crawler for Fundamental analysis platform for BOVESPA stocks, generating a score for each share according to the selected criteria on the indicators.
Technical Analysis library in pandas for backtesting algotrading and quantitative analysis
Collection of various algorithms in mathematics, machine learning, computer science, physics, etc implemented in C for educational purposes.
Solving the MNIST handwritten dataset with pure C using the free open source neural network library "fann".
Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.
Stock investment can be one of the ways to manage one’s asset.
Golub, Glattfelder and Olsen, ''The Alpha Engine: Designing an Automated Trading Algorithm''
Datasets, tools, and benchmarks for representation learning of code.
A complete computer science study plan to become a software engineer.
Solutions for various coding/algorithmic problems and many useful resources for learning algorithms and data structures
This guide is for those who know some math, know some programming language and now want to dive deep into deep learning
Programming Assignments and Lectures for Stanford's CS 231: Convolutional Neural Networks for Visual Recognition
Lecture slides and quizzes for Leskovec, Rajaraman, and Ullman's "Mining of Massive Datasets" Stanford course
Jupyter Notebooks and code for Derivatives Analytics with Python (Wiley Finance) by Yves Hilpisch.
Deep Hedging Demo - An Example of Using Machine Learning for Derivative Pricing.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.