Pinned Repositories
chunkdot
Multi-threaded matrix multiplication and cosine similarity calculations for dense and sparse matrices. Appropriate for calculating the K most similar items for a large number of items by chunking the item matrix representation (embeddings) and using Numba to accelerate the calculations.
coursera-machine-learning-AndrewNg-Python
This contains notes and exercises made in Python I made a long time ago from the Andrew Ng course in Coursera.
data-science-summit-2016
Python notebooks for the tutorial given in the Data Science Summit 2016 in Jerusalem
elitist-shuffle
In today's high pace user experience it is expected that new recommended items appear every time the user opens the application, but what do to if your recommendation system runs every hour or every day? I give you a solution/hack that you can plug & play without having to re-engineer your recommendation system.
PyData
Notebooks from the Face Recognition Tutorial I gave at PyData Amsterdam
PyDataAmsterdam2018
Contents of the workshop "Hands-on introduction to Deep Learning with Keras and Tensorflow" I gave at PyData Amsterdam 2018
sparkml-base-classes
rragundez's Repositories
rragundez/chunkdot
Multi-threaded matrix multiplication and cosine similarity calculations for dense and sparse matrices. Appropriate for calculating the K most similar items for a large number of items by chunking the item matrix representation (embeddings) and using Numba to accelerate the calculations.
rragundez/PyDataAmsterdam2018
Contents of the workshop "Hands-on introduction to Deep Learning with Keras and Tensorflow" I gave at PyData Amsterdam 2018
rragundez/PyData
Notebooks from the Face Recognition Tutorial I gave at PyData Amsterdam
rragundez/coursera-machine-learning-AndrewNg-Python
This contains notes and exercises made in Python I made a long time ago from the Andrew Ng course in Coursera.
rragundez/data-science-summit-2016
Python notebooks for the tutorial given in the Data Science Summit 2016 in Jerusalem
rragundez/app-skeleton
Python Flask application skeleton with an input form, using gunicorn and with a Dockerfile template
rragundez/sparkml-base-classes
rragundez/build-face-dataset
Script to retrieve all the faces found in pictures inside a directory
rragundez/jupyterhub-docker
rragundez/pybasler
This repository includes a Python wrapper over C++ to capture images from a basler camera. Uses the pylon c++ api.
rragundez/rragundez.github.io
rragundez/elitist-shuffle
In today's high pace user experience it is expected that new recommended items appear every time the user opens the application, but what do to if your recommendation system runs every hour or every day? I give you a solution/hack that you can plug & play without having to re-engineer your recommendation system.
rragundez/multi-threshold-neuron
Explanation and code implementation of the multi-threshold neuron in an artificial neural network
rragundez/udacity-deep-learning-google
Notes and notebooks of the Deep Learning course by google available in Udacity
rragundez/data-science-project-flow
rragundez/deep-painting
rragundez/dissect-vaes
Walk through lecture from the basics of information theory and Bayesian inference, to Variational Autoencoders.
rragundez/keras-core
A multi-backend implementation of the Keras API, with support for TensorFlow, JAX, and PyTorch.
rragundez/magic-modules
Add Google Cloud Platform support to Terraform
rragundez/pyspark-adhocml
PySpark Models that can be instantiated without calling fit on a estimator. Created ad-hoc with external parameters. Can be use as a normal Model in PySpark ML
rragundez/rush-hour-game-solver
Python script to solve the Rush Hour Solver game. It contains executable files that solve the board designed in board.txt
rragundez/utility-functions
Collection of generic functions I have used in my projects