A. Celis's repositories

awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

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BayesianOptimization

A Python implementation of global optimization with gaussian processes.

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CE7454_2019

Deep learning course CE7454, 2019

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cpp_project_template

C++ Project Template using CMake, Google Test, and Doxygen.

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flask-neo4j

Flask extension providing integration with the Neo4j graph database.

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gensim

Topic Modelling for Humans

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google-taxonomy-matcher

Matches a category of Google's Taxonomy to product that is described in any kind of text data

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hello_search

A search and recommender system based on Elasticsearch, Neo4j, Flask, Apache

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introduction_to_ml_with_python

Notebooks and code for the book "Introduction to Machine Learning with Python"

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joelnet

live coding deep learning library

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katas

:school: Coding katas

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ML-From-Scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

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mlhep2019

MLHEP'19 slides and notebooks

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movies-python-bolt

Neo4j Example application with flask backend using the neo4j-python-driver

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neo4j-flask

Flaskr Extended with Neo4j and Py2neo.

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numpy-ml

Machine learning, in numpy

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nyt-comments

New York Times graph with toy collaborative filtering recommender

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ouroboros

A standalone, pure Python implementation of the Python Standard Library.

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poincare-embedding-using-gensim

Train poincare embedding using gensim

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PRML

PRML algorithms implemented in Python

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PyPattyrn

A simple library for implementing common design patterns.

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PySpark-and-MLlib

Getting start with PySpark and MLlib

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Python-Flask-neo4j-Heroku-Example

Example of using Python, Flask and neo4j on Heroku

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python-patterns

Source code behind the python-patterns.guide site by Brandon Rhodes

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simple_rl

A simple framework for experimenting with Reinforcement Learning in Python.

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sphinx-tutorial

Exercises for the Sphinx Tutorial that I used to present each year at PyCon

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torchdiffeq

Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

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