BrainNetworksCourse / Brain Networks Course

Utility to generate plots with categorical variables using Altair.

Tutorials on Python Data Containers for no-so-BigData

Pre-trained Mongolian BERT models

deeplearn.js meets p5

Association Rule Mining from Spatial Data for Crime Analysis

Collection of useful notebooks to be used with the Spark Notebook (https://github.com/andypetrella/spark-notebook)

Entry for Grab's AI for S.E.A. challenge

Reproduced papers from the Reproducibility Zoo

The fastest way for developers and managers to gain practical ML knowledge and to apply it to their own projects.

Contains solutions to AV competitions

An implementation for attention model in Keras for sequence to sequence model.

Merge and Acquisitions Prediction based on M&A information from Crunchbase.

Cooperative Cut is a Markov Random Field inference method with high-order edge potentials.

Keras implementation of the multi-channel cascaded architecture introduced in the paper "Brain Tumor Segmentation with Deep Neural Networks"

Pioneering the design of materials to harness heat.

I have tried some of the machine learning and deep learning algorithm for IDS 2017 dataset. The link for the dataset is here: http://www.unb.ca/cic/datasets/ids-2017.html. By keeping Monday as the training set and rest of the csv files as testing set, I tried one class SVM and deep CNN model to check how it works. Here the Monday dataset contains only normal data and rest of the days contains both normal and attacked data. Also, from the same university (UNB) for the Tor and Non Tor dataset, I tried K-means clustering and Stacked LSTM models in order to check the classification of multiple labels.

Mixtures of Probabilistic Principal Component Analysers implementation in python

This repo includes code (written in Python) for Caltech-UCSD Birds-200-2011 dataset classification. I have used PyTorch Library for CNN's. You can download the dataset here http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz

Package Installer (pip) UI for JupyterLab