Name: Vinicius Caridá
Type: User
Bio: Using Tech, Data & AI to leverage business and impact people for a more fair and evolved world. "Happiness Only Real When Shared"
Twitter: vfcarida
Location: www.youtube.com/user/viniciuscarida
Blog: https://www.linkedin.com/in/viniciuscarida/
Vinicius Caridá's Projects
Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker
This repo contains annotated research papers that I found really good and useful
A list of software and papers related to automatic and fast Exploratory Data Analysis
:sunglasses: A curated list of awesome MLOps tools
A curated list of awesome transformer models.
AWS UGSP ML Zero to Hero
AWS Community Day 2021 website repository.
📚 Learn ML with clean code, simplified math and illustrative visuals.
Can NetGAN be improved on short random walks?
CI/CD Pipeline for machine learning with online training in Kubeflow
Curso pocket NLP
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.
code for Data Science From Scratch book
AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker
Data Science Tutorials
Data Visualization with Python
ESPM | Ciencia de Dados Aplicada aos Negocios
A Python package to assess and improve fairness of machine learning models.
Aulas / Lectures
A demo for a financial services bot
Gemma e Gemini Demos
This repository consists for gpu bootcamp material for HPC and AI
Hands-On Gradient Boosting with XGBoost and Scikit-learn Published by Packt
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Enterprise Scale NLP with Hugging Face & SageMaker Workshop series
Fit interpretable models. Explain blackbox machine learning.
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
KDD19 Tutorial: From Shallow to Deep Language Representations: Pre-training, Fine-tuning, and Beyond