Bicubic interpolation for PyTorch

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

S.M.Ali Eslam et.al. Attend, Infer, Repeat: Fast Scene Understanding with Generative Models ICML16

Empirical Study of Recent Face Alignment Methods

My solutions for Udacity's "Introduction to Computer Vision" MOOC

給想體驗如何在微軟AZ notebook上操作GPU及做圖像辨識(以MNIST資料集為例)

Utility to generate plots with categorical variables using Altair.

Deployment template for Azure WebApp, CNTK, Python 3 (x64) and sample model

Sentiment Analysis with LSTMs in Tensorflow

This is just a collection of projects that made during my DEEPLEARNING NANODEGREE by UDACITY

Bayesian methods in deep learning Summer School

Home of the Semi-Analytic Galaxy Evolution (SAGE) galaxy formation model

Creating a stylised pseudo-3D exoplanet map using matplotlib

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

Handwritten text recognition with Keras

All the notebooks for the analysis of Emotional Arcs within the Project Gutenberg corpus, see "The emotional arcs of stories are dominated by six basic shapes"

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

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.

Notes and experiments to understand deep learning concepts