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

Association Rule Mining from Spatial Data for Crime Analysis

Package Installer (pip) UI for JupyterLab

Utility to generate plots with categorical variables using Altair.

Tutorials on Python Data Containers for no-so-BigData

Sentiment Analysis with LSTMs in Tensorflow

Pioneering the design of materials to harness heat.

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"

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

Mixtures of Probabilistic Principal Component Analysers implementation in python

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.

Contains solutions to AV competitions

Code for our ICAR 2019 paper "ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection"

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

Pre-trained Mongolian BERT models

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