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api

The API for Cellenics, staying in between the UI and the backends (worker, pipeline)

books

Python server to store information about read books.

books_tldr

A personal selection of the most memorable or just random bits about books I've read.

cart_and_random_forests

CART and Random Forests in Python 3.5

chinese-character-generator

Generate dynamic Chinese character learning wallpapers with stroke order, Pinyin & Zhuyin (Bopomofo) annotations, and english definition. Perfect for language learners looking to immerse themselves in Chinese script.

code2vec

MLonCode community effort to implement Learning Distributed Representations of Code (https://arxiv.org/pdf/1803.09473.pdf)

contextuallstm

Contextual LSTM for NLP tasks like word prediction and word embedding creation for Deep Learning

delete_from_another_repo

Github action to delete path from another repository

diann

Named entity recognition in python for disability annotation on documents from the medical domain.

distributed_rdf

fastbook_sols

(My) Solutions to Fastbook chapters

final-degree-project

Computer Science's Degree Project

golang-ads

Golang Advanced Data Structures

hpc.fassr

High-Performance Computing. Fundamental Analysis Stock Screener and Ranker

iac

Our Infrastructure as Code and GitOps repository.

inframock

A mocked local copy of the Cellenics AWS stack to ease local development and testing.

kafkasl.github.io

Pol Avec's blog

lilly

Sample Android application to get information about nearby locations and display it on a list and map

mypapers.ai

obnk

Example of small REST API for Online BaNKing created with django rest framework.

patents_parser

Project aimed to parse and create a patent's exchange database record.

pipeline

The Cellenics pipeline project for dependency-managed work processing.

pipeoperator

Example of how to implement a Pool of threads with fork and pipes on Python to compute arithmetic operations

pyproct

pyProCT is an open source cluster analysis software especially adapted for jobs related with structural proteomics. Its approach allows users to define a clustering goal (clustering hypothesis) based on their domain knowledge. This hypothesis will guide the software in order to find the best algorithm and parameters (including the number of clusters) to obtain the result that fulfills their expectatives. In this way users do not need to use cluster analysis algorithms as a black box, improving this way the results. pyProCT not only generates a resulting clustering, it also implements some use cases like the extraction of representatives or trajectory redundance elimination.

random_name

A simple library for generating random names.

randomized-binary-search-trees

"Randomized Binary Search Tree" monolythic class implementation

scala_classifiers

Scala repository implementing basic classifiers Max a Posteriori and Naive Bayes, and more advanced method Tree Augmented Naive Bayes

solutions-terraform-cloudbuild-gitops

source-code-analysis

dev repo, see https://github.com/src-d/code2vec

talk2apis

Talk and use your APIs using only natural language thanks to LLMs and HTTP integration.