Discover @rragundez Open Source projects

Rodrigo Agundez (@rragundez)

rragundez

Stars
259
Global Rank 100,320 (Top 4 %)
Followers 111
Registered almost 9 years ago
Most used languages

Jupyter Notebook
57.1 %

Python
35.7 %

C++
7.1 %
Location 🇸🇬 Singapore
Country Total Rank 693
Country Ranking

Jupyter Notebook
46

Python
438

C++
618

PyDataAmsterdam2018

Contents of the workshop "Hands-on introduction to Deep Learning with Keras and Tensorflow" I gave at PyData Amsterdam 2018

Jupyter Notebook

PyData

Notebooks from the Face Recognition Tutorial I gave at PyData Amsterdam

Jupyter Notebook

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.

Jupyter Notebook

data-science-summit-2016

Python notebooks for the tutorial given in the Data Science Summit 2016 in Jerusalem

Jupyter Notebook

app-skeleton

Python Flask application skeleton with an input form, using gunicorn and with a Dockerfile template

rragundez.github.io

build-face-dataset

Script to retrieve all the faces found in pictures inside a directory

sparkml-base-classes

Jupyter Notebook

pybasler

This repository includes a Python wrapper over C++ to capture images from a basler camera. Uses the pylon c++ api.

jupyterhub-docker

udacity-deep-learning-google

Notes and notebooks of the Deep Learning course by google available in Udacity

Jupyter Notebook

multi-threshold-neuron

Explanation and code implementation of the multi-threshold neuron in an artificial neural network

Jupyter Notebook

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.

Jupyter Notebook