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Timos Kouloumpis's Projects

amazon-dsstne icon amazon-dsstne

Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models

analytics_vidhya icon analytics_vidhya

Codes related to activities on AV including articles, hackathons and discussions.

aredis icon aredis

An efficient and user-friendly async redis client

awesome-bigdata icon awesome-bigdata

A curated list of awesome big data frameworks, ressources and other awesomeness.

awesome-datascience icon awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

awesome-msr icon awesome-msr

A curated repository of software engineering repository mining data sets

clikraken icon clikraken

Command-line client for the Kraken exchange

comp-finance icon comp-finance

This repository contains Python-based tools for Computational Finance. It is related to the Computational Finance blog run by Stuart Reid (www.stuartreid.co.za/blog).

data-science-on-gcp icon data-science-on-gcp

Source code accompanying book: Data Science on the Google Cloud Platform, Valliappa Lakshmanan, O'Reilly 2017

finmarketpy icon finmarketpy

Python library for backtesting trading strategies and analyzing financial markets (formally pythalesians)

forex_algotrading icon forex_algotrading

My Forex algotrading platform in Python - based on my posts at http://jon.io

hadoop-book icon hadoop-book

Example source code accompanying O'Reilly's "Hadoop: The Definitive Guide" by Tom White

horovod icon horovod

Distributed training framework for TensorFlow, Keras, and PyTorch.

jpmml icon jpmml

Java PMML API (legacy codebase)

kedro icon kedro

A Python library that implements software engineering best-practice for data and ML pipelines.

knime-scripting icon knime-scripting

knime-scripting includes scripting extensions for KNIME to integrate R, Matlab, Python and Groovy scripts. These extensions include a collection of nodes to develop and deploy templates in the respective languages.

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