Word2vec Workshopword2vec workshop - a conceptual introduction and practical application
Stars: ✭ 21 (-27.59%)
TechtalksSlides and Supplementary Material of the past TechTalks at the Karlsruhe Machine Learning, Statistics and AI Meetup
Stars: ✭ 21 (-27.59%)
SidOfficial implementation for ICCV19 "Shadow Removal via Shadow Image Decomposition"
Stars: ✭ 28 (-3.45%)
Glo4030 LabsLaboratoires du cours GLO-4030/GLO-7030
Stars: ✭ 21 (-27.59%)
GpufilterGPU Recursive Filtering
Stars: ✭ 28 (-3.45%)
Lab04Web scraping, APIs, and Twitter
Stars: ✭ 21 (-27.59%)
KispythonКурс программирования на языке Python
Stars: ✭ 27 (-6.9%)
AnatomyofmatplotlibAnatomy of Matplotlib -- tutorial developed for the SciPy conference
Stars: ✭ 943 (+3151.72%)
Repro Zoo 2018Reproduced papers from the Reproducibility Zoo
Stars: ✭ 21 (-27.59%)
WpthermlPioneering the design of materials to harness heat.
Stars: ✭ 21 (-27.59%)
Sgdoptim.jlA julia package for Gradient Descent and Stochastic Gradient Descent
Stars: ✭ 27 (-6.9%)
AvContains solutions to AV competitions
Stars: ✭ 21 (-27.59%)
Coop CutCooperative Cut is a Markov Random Field inference method with high-order edge potentials.
Stars: ✭ 20 (-31.03%)
MppcaMixtures of Probabilistic Principal Component Analysers implementation in python
Stars: ✭ 19 (-34.48%)
Machine Learning Starter KitThe fastest way for developers and managers to gain practical ML knowledge and to apply it to their own projects.
Stars: ✭ 20 (-31.03%)
Crunchbase MlMerge and Acquisitions Prediction based on M&A information from Crunchbase.
Stars: ✭ 20 (-31.03%)
Seq2seq Attention ModelAn implementation for attention model in Keras for sequence to sequence model.
Stars: ✭ 20 (-31.03%)
Mambo Stars: ✭ 27 (-6.9%)
Brain Tumor Segmentation KerasKeras implementation of the multi-channel cascaded architecture introduced in the paper "Brain Tumor Segmentation with Deep Neural Networks"
Stars: ✭ 20 (-31.03%)
Intrusion Detection SystemI 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.
Stars: ✭ 20 (-31.03%)
Sdtm mapperAI SDTM mapping (R for ML, Python, TensorFlow for DL)
Stars: ✭ 27 (-6.9%)
AndaCode for our ICAR 2019 paper "ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection"
Stars: ✭ 20 (-31.03%)
ResimnetImplementation of ReSimNet for drug response similarity prediction
Stars: ✭ 28 (-3.45%)
Stat406STAT406 @ UBC - "Elements of Statistical Learning"
Stars: ✭ 27 (-6.9%)
Mj583J583 Advanced Interactive Media
Stars: ✭ 15 (-48.28%)
Attend Infer Repeat PytorchS.M.Ali Eslam et.al. Attend, Infer, Repeat: Fast Scene Understanding with Generative Models ICML16
Stars: ✭ 15 (-48.28%)
Seq 2 Seq OcrHandwritten text recognition with Keras
Stars: ✭ 15 (-48.28%)
ChexpertCheXpert competition models -- attention augmented convolutions on DenseNet, ResNet; EfficientNet
Stars: ✭ 28 (-3.45%)
Tensorflow2 Generative ModelsImplementations of a number of generative models in Tensorflow 2. GAN, VAE, Seq2Seq, VAEGAN, GAIA, Spectrogram Inversion. Everything is self contained in a jupyter notebook for easy export to colab.
Stars: ✭ 883 (+2944.83%)
TutorialsA project for developing tutorials for Streams
Stars: ✭ 14 (-51.72%)
Movie recommenderMovieLens based recommender system.使用MovieLens数据集训练的电影推荐系统。
Stars: ✭ 914 (+3051.72%)
NotebooksAn attempt to formalize my thoughts. A pythonic approach to mental housekeeping
Stars: ✭ 14 (-51.72%)
MotivesextractorExtract Polyphonic Musical Motives from Audio Recordings
Stars: ✭ 14 (-51.72%)
DalrImplementation of "Domain-adaptive deep network compression", ICCV 2017
Stars: ✭ 28 (-3.45%)