Computational Suite for Bioinformaticians and Biologists (CSBB) is a RShiny application developed with an intention to empower researchers from wet and dry lab to perform downstream Bioinformatics analysis
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Updated
Apr 5, 2023 - R
Computational Suite for Bioinformaticians and Biologists (CSBB) is a RShiny application developed with an intention to empower researchers from wet and dry lab to perform downstream Bioinformatics analysis
FxNorm-Automix - Implementation of automatic music mixing systems. We show how we can use wet music data and repurpose it to train a fully automatic mixing system
The data preprocessing pipeline for the VISION project (for mouse data)
Machine Learning Nano-degree Project : To help a charity organization identify people most likely to donate to their cause
Contact data normalization adapted from the Empreinte Sociométrique's normalizers
I made various data normalization operations with python scripts. Target data in CSV format
An exploratory data analysis on a global terrorism dataset
A Simple example to show Why Data Normalization is necessary for Deep Learning
Preprocess bibliographical data for alignement tasks
Feature wise normalization: An effective way of normalizing data
A pipeline breaking down a spreadsheet of crowdfunding data into 3NF and building a PostgreSQL database from it.
Adventure Works Bike Shop Analysis using Power BI
A collection of bioinformatics and data mining scripts
Trabalhos de Inteligência Computacional Aplicada
Database Development with SQL
Prevendo o Nível de Satisfação dos Clientes do Santander.
Web app to fetch artists events data via public API. Managing global state with redux-toolkit. Responsive design with material-ui. Cool animations and transitions
data rescaling, normalization and standardization techniques
Classified images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset was preprocessed, then trained a convolutional neural network on all the samples. I normalized the images, one-hot encoded the labels, built a convolutional layer, max pool layer, and fully connected layer.
PKnorm: a nonlinear model for sequencing depth and signal-to-noise ratio normalization between epigenomic data
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