Text summarization with tensorflowImplementation of a seq2seq model for summarization of textual data. Demonstrated on amazon reviews, github issues and news articles.
Stars: ✭ 226 (-98.78%)
Practical 1Oxford Deep NLP 2017 course - Practical 1: word2vec
Stars: ✭ 220 (-98.81%)
Sohu competitionSohu's 2018 content recognition competition 1st solution(搜狐内容识别大赛第一名解决方案)
Stars: ✭ 224 (-98.79%)
Amazing Feature EngineeringFeature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
Stars: ✭ 218 (-98.83%)
SkyliftWi-Fi Geolocation Spoofing with the ESP8266
Stars: ✭ 223 (-98.8%)
Hacktoberfest2020A repo for new open source contributors to begin with open source contribution. Contribute and earn awesome swags.
Stars: ✭ 221 (-98.81%)
Source separationDeep learning based speech source separation using Pytorch
Stars: ✭ 226 (-98.78%)
Kitti DatasetVisualising LIDAR data from KITTI dataset.
Stars: ✭ 217 (-98.83%)
Video to bvhConvert human motion from video to .bvh
Stars: ✭ 222 (-98.8%)
Python AwesomeLearn Python, Easy to learn, Awesome
Stars: ✭ 219 (-98.82%)
Dat7General Assembly's Data Science course in Washington, DC
Stars: ✭ 227 (-98.78%)
Research Paper NotesNotes and Summaries on ML-related Research Papers (with optional implementations)
Stars: ✭ 218 (-98.83%)
Pixel level land classificationTutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.
Stars: ✭ 217 (-98.83%)
Lrp toolboxThe LRP Toolbox provides simple and accessible stand-alone implementations of LRP for artificial neural networks supporting Matlab and Python. The Toolbox realizes LRP functionality for the Caffe Deep Learning Framework as an extension of Caffe source code published in 10/2015.
Stars: ✭ 225 (-98.79%)
50 Days Of MlA day to day plan for this challenge (50 Days of Machine Learning) . Covers both theoretical and practical aspects
Stars: ✭ 218 (-98.83%)
Triplet AttentionOfficial PyTorch Implementation for "Rotate to Attend: Convolutional Triplet Attention Module." [WACV 2021]
Stars: ✭ 222 (-98.8%)
DatascienceprojectsThe code repository for projects and tutorials in R and Python that covers a variety of topics in data visualization, statistics sports analytics and general application of probability theory.
Stars: ✭ 223 (-98.8%)
PaddlehelixBio-Computing Platform featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集
Stars: ✭ 213 (-98.85%)
OwnphotosSelf hosted alternative to Google Photos
Stars: ✭ 2,587 (-86.06%)
Malware DetectionMalware Detection and Classification Using Machine Learning
Stars: ✭ 217 (-98.83%)
NemoNeMo: a toolkit for conversational AI
Stars: ✭ 3,685 (-80.14%)
TensorflowDeep Learning Zero to All - Tensorflow
Stars: ✭ 216 (-98.84%)
SecSeed, Expand, Constrain: Three Principles for Weakly-Supervised Image Segmentation
Stars: ✭ 221 (-98.81%)
Text ClassificationText Classification through CNN, RNN & HAN using Keras
Stars: ✭ 216 (-98.84%)
NotebookerProductionise your Jupyter Notebooks as easily as you wrote them.
Stars: ✭ 215 (-98.84%)
HyperspectralDeep Learning for Land-cover Classification in Hyperspectral Images.
Stars: ✭ 215 (-98.84%)
Pytorch SuperpointSuperpoint Implemented in PyTorch: https://arxiv.org/abs/1712.07629
Stars: ✭ 214 (-98.85%)
HtmresearchExperimental algorithms. Unsupported.
Stars: ✭ 221 (-98.81%)
Pytorch ByolPyTorch implementation of Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Stars: ✭ 213 (-98.85%)
TutmomTutorial on "Modern Optimization Methods in Python"
Stars: ✭ 214 (-98.85%)
PythonnumericaldemosWell-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
Stars: ✭ 213 (-98.85%)
Dl For ChatbotDeep Learning / NLP tutorial for Chatbot Developers
Stars: ✭ 221 (-98.81%)
Kekoxtutorial전 세계의 멋진 케라스 문서 및 튜토리얼을 한글화하여 케라스x코리아를 널리널리 이롭게합니다.
Stars: ✭ 213 (-98.85%)
Example ScriptsExample Machine Learning Scripts for Numerai's Tournament
Stars: ✭ 223 (-98.8%)
Pytorch Handbookpytorch handbook是一本开源的书籍,目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门,其中包含的Pytorch教程全部通过测试保证可以成功运行
Stars: ✭ 15,817 (-14.75%)
Decaf ReleaseDecaf is DEPRECATED! Please visit http://caffe.berkeleyvision.org/ for Caffe, the new framework that has all the good things: GPU computation, full train/test scripts, native C++, and an active community!
Stars: ✭ 227 (-98.78%)
Image classification with 5 methodsCompared performance of KNN, SVM, BPNN, CNN, Transfer Learning (retrain on Inception v3) on image classification problem. CNN is implemented with TensorFlow
Stars: ✭ 227 (-98.78%)
Tf VqvaeTensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).
Stars: ✭ 226 (-98.78%)