YujinChoi0410

YujinChoi0410's Stars

• hbatmit/ns2.35

  ns2 for research

  Language:C++48102

• guillaume-chevalier/LSTM-Human-Activity-Recognition

  Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

  Language:Jupyter Notebook3.3k934

• servomac/Human-Activity-Recognition

  Use a LSTM network to predict human activities from sensor signals collected from a smartphone

  Language:Python4725

• Astuary/LSTMVariantsForTimeSeriesForecasting

  I have used the ExtraSensory data set. This is a human activity recognition data set containing data from 60 individuals. The task that I will focus on, is probabilistic activity forecasting. Given a sub-dataframe consisting of between one and 30 consecutive observations for a single individual and a timestamp value t, the objective is to predict the (log) probability that each of the five labels (label:LYING_DOWN, label:SITTING, label:FIX_walking, label:TALKING, label:OR_standing) is active (e.g., takes the value 1) at the future specified time, t.

  Language:Python1

• bijaykahar/Walking-Running-Prediction

  Rubixe Internship Learning Project

  Language:Jupyter Notebook1

• ShaoxiongYuan/PycharmProjects

  Tarena Education

  Language:Jupyter Notebook31

• saugatapaul1010/Human-Activity-Recognition-using-classical-Machine-Learning-and-LSTM

  This experiments predicts a human activity based on the data it recieves from the gyroscope. The model classifies a human activity in one of the 6 classes - Sitting, Standing, Walking, Walking Downstairs, Walking Upstairs, Laying.

  Language:Jupyter Notebook1

• Akshaykumarcp/Human-Activity-Recognition-ML-DL

  Human Activity Recognition application using ML & DL

  Language:Python1

• ANDROID564/LSTM_prediction

  Predict the next future steps using LSTM. From a csv file data values are read and then future steps are predicted and saved in a csv file

  Language:Python1

• WegraLee/deep-learning-from-scratch-2

  『밑바닥부터 시작하는 딥러닝 ❷』(한빛미디어, 2019)

  Language:Python250254

• yschoi9930/Deep-Learnig-from-Scratch

  밑바닥부터 시작하는 딥러닝 책 독학 코딩

  Language:Jupyter Notebook1

• nicodjimenez/lstm

  Minimal, clean example of lstm neural network training in python, for learning purposes.

  Language:Python1.7k654

• brian-team/brian

  Brian is a simulator for spiking neural networks available on almost all platforms. This is the legacy version that is no longer developed, for new projects consider using Brian2 instead.

  Language:Python6022

• reclosedev/async_gui

  Easy threading and multiprocessing for GUI applications

  Language:Python5712

• Terrabits/tkinter-multiprocessing-example

  Example python application with main process tkinter GUI and second worker process.

  Language:Python51

• bslatkin/ringbuffer

  Ring buffer that allows for high-throughput data transfer between multiproccessing Python processes.

  Language:Python12525

• eric-wieser/numpy_ringbuffer

  Ring-buffer implementation that thinly wraps a numpy array

  Language:Python8215

• al-jshen/csv_to_dataloader

  read a csv file and turn it into a pytorch dataloader ready to be used for training a network

  Language:Python2

• yysijie/st-gcn

  Spatial Temporal Graph Convolutional Networks (ST-GCN) for Skeleton-Based Action Recognition in PyTorch

  Language:Python1.5k335

• Dosomecrazy/video2bvh

  Extracts human motion in video and save it as bvh mocap file.

  Language:Python4715

• Dene33/video_to_bvh

  Convert human motion from video to .bvh

  Language:Jupyter Notebook363112

• mk-minchul/attend-and-compare

  Language:Python255

• biomedia-mira/istn

  Image-and-Spatial Transformer Networks

  Language:Python10826

• and-jonas/LesionZoo

  Language:Python1

• Artur112/Brain_Tumour_Segmentation

  Code for training a 3DUnet for Brain tumour segmentation from Brats 2019 dataset; for feature extraction from the segmented volumes and for survival prediction. Run train.py for training, segment.py for segmenting test scans and evaluate.py for evaluating the performance of those segmentations. Basic code also written to perform survival prediction with a random forest classifiier.

  Language:Python218

• mlnotebook/domain_adapation_istn

  Adversarially training Image and Spatial Transformer Networks to perform Domain Adapation

  Language:Python132

• JunMa11/MICCAI-OpenSourcePapers

  MICCAI 2019-2023 Open Source Papers

  1.2k231

• ver228/cell_localization

  Language:Python1

• ai-med/squeeze_and_excitation

  PyTorch Implementation of 2D and 3D 'squeeze and excitation' blocks for Fully Convolutional Neural Networks

  Language:Python31052

• tranleanh/modified-resunet

  Modified Residual U-Net (ResUnet) for Image Segmentation

  Language:Python3811