guilhermebonaldo

estatistica-das-redes-sociais

This project is based on MAE5908 - Estatística de Redes Sociais (2020) course, part of Universidade de São Paulo Statistics department (IME-USP) master's program.

dog_app

Udacity Deep Learning project

aind2-cnn

AIND Term 2 -- Lesson on Convolutional Neural Networks

boston_housing

Projeto do curso Engenheiro de machine learning - Udacity

ClusterCardinality

Code for the clustering algorithm

deploy_completo_AWS

stockz

A Complete Operational Metrics Mechanism for Useful Notification of Investments and Stocks Management (C.O.M.M.U.N.I.S.M.)

Trabalho-Final-UDACITY

Detecção de tumores em imagens histológicas de mamas utilizando redes neurais profundas

unsupervised_anomaly_detection

cml_churn_demo

Customer_segments

project of unsupervised learning from Udacity

ddpg-reacher

Implementation of Deep Deterministic Policy Gradient for Continuous Control to solve Reacher environment

deepQLearningBananaGrabber

An implementation of the Deep Q-Learning algorithm to optimize navigation in the BananaGrabber environment

ethereum_future

This is the Code for "Ethereum Future Prices" by Siraj Raval on Youtube

experimental-lab

Some codes, some ideas, somo exploration by bizubox

face_classification

Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.

feature-engineering-book

Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018

live_coding_udacity_ceaps

Arquivos utilizados no Live Coding proporcionado pelo Udacity onde tratamos os dados do CEAPS

modelagem-bayesiana

modeldrift

Capturing model drift and handling its response - Example webinar

notebooks_tutoriais

Aqui você encontrar notebooks para alguns vídeos do meu canal no Youtube

potholes-mapping

An algorithm that ranks potholes as the cars pass through them.

rede_neural_feedfoward_MATLAB

Script em Matlab para gerar e treinar uma rede neural feedfoward

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

student_intervention

Projeto do curso Engenheiro de machine learning - Udacity

telcochurn-demo

time-series-classification-and-clustering

Time series classification and clustering code written in Python. Mostly based on the work of Dr. Eamonn Keogh at University of California Riverside

time_series_prediction

Titanic-Kaggle

Code for Titanic Kaggle competition

ud120-projects

Starter project code for students taking Udacity ud120