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Contains Jupyter notebooks associated with the "Deep Reinforcement Learning Tutorial" tutorial given at the O'Reilly 2017 NYC AI Conference.
Reproducible Data Science at Scale!
Example for creating an API from start to end
Advanced Pandas Vault — Utilities, Functions and Snippets (by @firmai).
AI/ML citation graph with postgres + graphql
Tutorial on scikit-learn and IPython for parallel machine learning
Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
pdf2audiobook
A set of tools for extracting tables from PDF files helping to do data mining on (OCR-processed) scanned documents.
Short Tutorial to Probabilistic Graphical Models(PGM) and pgmpy
Pharmaceutical Sales prediction across multiple stores. End-to-end product that delivers this prediction using Streamlit.
PipelineAI Kubeflow Distribution
Sample Python Django application for Azure Pipelines docs
A deep learning based system for disorder detection in tomato plants. Also used IoT to get sensor data from the plants. Two seperate models were trained for this project. One dealt with Images and other with Sensor Data. You can use both to predict the Disorder of Tomato Plants.
Fuzzy string matching, grouping, and evaluation.
ML implementations for practical use
Practical Python Programming (course by @dabeaz)
Code repository for O'Reilly book
Practical Time-Series Analysis, published by Packt
DL course co-developed by YSDA, HSE and Skoltech
A course in reinforcement learning in the wild
深度学习算法实战
The easiest way to automate your data
Various material around private machine learning, some associated with blog
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
A guideline for building practical production-level deep learning systems to be deployed in real world applications.
Examples from Programming Collective Intelligence
Training keras/xgboost model to solve projecteuler.net captchas with training data provided by 2captcha
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.