FernandoFavoretti

apriori_algorithm

Application of apriori rule algorithm

aWhere

aWhere API python wrapper

bigode

BMSIS-OMNICROP

OMNICROP uses a database composed of labeled data such as light quality and quantity, general climate characteristics and soil quality from successful farming sites. This data is then used to sort and select the most successful crops for a particular set of circumstances. This process can provide either earthbound farmers or off-world colonists with the a-priori information needed to start successful, productive food production while preventing costly and time-consuming mistakes that derive from on-site experimentation or be directly linked to self-managing cultivation chambers. This project is an open source model where any user can input data and obtain results. Frequent reuse will assist the models to “learn” and continually re-calibrate itself providing increasingly accurate results over time. If selected, the candidate will assist with organizing the labeled data and on programming the model as well as be given full liberty to come up with new ideas on how to integrate the model’s premise on building cheap self-managing cultivation chambers using IoT.

DeepLearning

Udacity Deep Learning Foundations Nanodegree

fernandofavoretti.github.io

master_AutoML

Uma solução automática de engenharia de características para algoritmos de aprendizado baseados em árvores.

mixture_of_experts

SDEP2

Ep de Sistemas Distribuidos usando Map Reduce

Udacity_Machine_Learning

Nanodegree of Machine Learning from udacity

FernandoFavoretti/fernandofavoretti.github.io

FernandoFavoretti/master_AutoML

Uma solução automática de engenharia de características para algoritmos de aprendizado baseados em árvores.

FernandoFavoretti/SDEP2

Ep de Sistemas Distribuidos usando Map Reduce

FernandoFavoretti/apriori_algorithm

Application of apriori rule algorithm

FernandoFavoretti/aWhere

aWhere API python wrapper

FernandoFavoretti/BMSIS-OMNICROP

OMNICROP uses a database composed of labeled data such as light quality and quantity, general climate characteristics and soil quality from successful farming sites. This data is then used to sort and select the most successful crops for a particular set of circumstances. This process can provide either earthbound farmers or off-world colonists with the a-priori information needed to start successful, productive food production while preventing costly and time-consuming mistakes that derive from on-site experimentation or be directly linked to self-managing cultivation chambers. This project is an open source model where any user can input data and obtain results. Frequent reuse will assist the models to “learn” and continually re-calibrate itself providing increasingly accurate results over time. If selected, the candidate will assist with organizing the labeled data and on programming the model as well as be given full liberty to come up with new ideas on how to integrate the model’s premise on building cheap self-managing cultivation chambers using IoT.

FernandoFavoretti/DeepLearning

Udacity Deep Learning Foundations Nanodegree

FernandoFavoretti/mixture_of_experts

FernandoFavoretti/pattern_recognition

FernandoFavoretti/Udacity_Machine_Learning

Nanodegree of Machine Learning from udacity

FernandoFavoretti/data-science-wg

SF Brigade's Data Science Working Group.

FernandoFavoretti/fast_api_skeleton

FernandoFavoretti/fklearn

fklearn: Functional Machine Learning

FernandoFavoretti/gitignore

A collection of useful .gitignore templates

FernandoFavoretti/incubator-airflow

Apache Airflow (Incubating)

FernandoFavoretti/jupyter-dark-theme

Dark theme for Jupyter Notebook (iPython 4) UI

FernandoFavoretti/liken

full project - crawler, python api, front

FernandoFavoretti/Machine-Learning-with-R-datasets

Formatted datasets for Machine Learning With R by Brett Lantz

FernandoFavoretti/miniflow

#Udacity nanodegree Deep Learning

FernandoFavoretti/ML4SETI

Machine Learning for SETI

FernandoFavoretti/newsfeed

Um leitor simples de RSS com possibilidade de curtir e compartilhar matérias que são expostas como RSS.

FernandoFavoretti/NMRview_data

FernandoFavoretti/nn-from-scratch

Implementing a Neural Network from Scratch

FernandoFavoretti/Palantir

PNC group project

FernandoFavoretti/PyAthena

PyAthena is a Python DB API 2.0 (PEP 249) compliant client for Amazon Athena.

FernandoFavoretti/python-sample-vscode-flask-tutorial

Sample code for the Flask tutorial in the VS Code documentation

FernandoFavoretti/teia_front_end_devmode

FernandoFavoretti/TOTVS_ml

Data Science challenge TOTVS

FernandoFavoretti/ud120-projects

Starter project code for students taking Udacity ud120

FernandoFavoretti/workshop_ml_each

Workshop de machine learning oferecido durante a Semana de Sistemas de Informação da EACH USP