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agile_data_code_2

Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition

anonyxel

Excel tables Anonymizer for Machine Learning applications.

art-dcgan

Modified implementation of DCGAN focused on generative art. Includes pre-trained models for landscapes, nude-portraits, and others.

arules_shared

developing an eclat library for python

azure-tdsp-utilities

Utilities and scripts developed as part of Microsoft's Team Data Science Process for productive data science

bigquery-integrations

Import files (data) from Intercom, FTP(S), SFTP, MySQL, etc. servers into BigQuery.

blockchain-test

carnegie-mellon

course-v3

The 3rd edition of course.fast.ai - coming in 2019

customer-segmentation

An example of clustering applied to finding customer segments.

customer360

Customer 360 analytics powered by MapR

customer_segmentation

Analysing the content of an E-commerce database that contains list of purchases. Based on the analysis, I develop a model that allows to anticipate the purchases that will be made by a new customer, during the following year from its first purchase.

customer_segmentation-1

Simple example code to show how to do customer segmentation. Enjoy it!

customersegmentation

By means of this project I am trying to create a value-based customer segmentation model using RFM(Recency, Frequency, Monetary) analysis in python using pandas, numpy and matplotlib

cycle-gan

A pytorch implementation of cycle gan.

cyclegan-tensorflow

Tensorflow implementation for learning an image-to-image translation without input-output pairs. https://arxiv.org/pdf/1703.10593.pdf

data-science-in-gaming

Some pet projects focused on gaming data

datadriven-customer-segmentation-with-python

generalized code used in my master thesis

datafest2020-geo-workshop

geo libs, hacks and practices

datasciencefromscratch

dataset

Dataset helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.

dds

delivs

delivs_1

django

dl-notebooks

dl_cnn_history

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

edu_cm_dl

edward

A library for probabilistic modeling, inference, and criticism. Deep generative models, variational inference. Runs on TensorFlow.