Alexandre-Caldeira
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Lecture Notes on Design & Analysis of Experiments
🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
This repository contains the Matlab code and data used to generate the results in the paper titled "A numerical approach for constructing adaptive designs with sample size re-estimation".
A Python Package For System Identification Using NARMAX Models
NMA Computational Neuroscience course
Code for RELAX, a framework for explaining representations.
😎 A curated list of awesome MLOps tools
alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, 2023, and 2024)
https://huyenchip.com/ml-interviews-book/
AGI's query engine - Platform for building AI that can learn and answer questions over federated data.
A beginner data engineering project which involves scrapping offers from https://www.mercadolivre.com.br/ofertas, stores in a postgres database and analyze the data scrapped.
Repositório para acompanhamento das atividades do terceiro processo seletivo da equipe NIAS-IA
Summaries and resources for Designing Machine Learning Systems book (Chip Huyen, O'Reilly 2022)
Repositório de passagem de conhecimento entre equipes do time NIASIA.
A Curated List of Azure Resources. The list provides you with enough resources to get a full overview of the services in Azure and get started with cloud computing.
A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin
The Fuzzy Labs guide to the universe of open source MLOps
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
MLOps using Azure ML Services and Azure DevOps
Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions
Azure Quickstart Templates
Best Practices, code samples, and documentation for Computer Vision.
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Nessa seção falamos sobre 22 (de 23) padrões de projeto da GoF. Todos descritos em vídeo, com teoria e prática + exemplos de código.
