HyperspectralDeep Learning for Land-cover Classification in Hyperspectral Images.
Stars: ✭ 215 (-93.3%)
Text ClassificationText Classification through CNN, RNN & HAN using Keras
Stars: ✭ 216 (-93.27%)
Tensorflow Without A PhdA crash course in six episodes for software developers who want to become machine learning practitioners.
Stars: ✭ 2,488 (-22.52%)
50 Days Of MlA day to day plan for this challenge (50 Days of Machine Learning) . Covers both theoretical and practical aspects
Stars: ✭ 218 (-93.21%)
Pytorch SuperpointSuperpoint Implemented in PyTorch: https://arxiv.org/abs/1712.07629
Stars: ✭ 214 (-93.34%)
Kitti DatasetVisualising LIDAR data from KITTI dataset.
Stars: ✭ 217 (-93.24%)
PythonnumericaldemosWell-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
Stars: ✭ 213 (-93.37%)
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 (-93.37%)
TfwssWeakly Supervised Segmentation with Tensorflow. Implements instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).
Stars: ✭ 212 (-93.4%)
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 (-93.24%)
NotebookerProductionise your Jupyter Notebooks as easily as you wrote them.
Stars: ✭ 215 (-93.3%)
Hacktoberfest2020A repo for new open source contributors to begin with open source contribution. Contribute and earn awesome swags.
Stars: ✭ 221 (-93.12%)
Pytorch ByolPyTorch implementation of Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Stars: ✭ 213 (-93.37%)
TutmomTutorial on "Modern Optimization Methods in Python"
Stars: ✭ 214 (-93.34%)
Kekoxtutorial전 세계의 멋진 케라스 문서 및 튜토리얼을 한글화하여 케라스x코리아를 널리널리 이롭게합니다.
Stars: ✭ 213 (-93.37%)
Python AwesomeLearn Python, Easy to learn, Awesome
Stars: ✭ 219 (-93.18%)
Neural decodingA python package that includes many methods for decoding neural activity
Stars: ✭ 212 (-93.4%)
Malware DetectionMalware Detection and Classification Using Machine Learning
Stars: ✭ 217 (-93.24%)
SquadBuilding QA system for Stanford Question Answering Dataset
Stars: ✭ 213 (-93.37%)
CardioCardIO is a library for data science research of heart signals
Stars: ✭ 218 (-93.21%)
TutorialsAI-related tutorials. Access any of them for free → https://towardsai.net/editorial
Stars: ✭ 204 (-93.65%)
Edavizedaviz - Python library for Exploratory Data Analysis and Visualization in Jupyter Notebook or Jupyter Lab
Stars: ✭ 220 (-93.15%)
Rl Adventure 2PyTorch0.4 implementation of: actor critic / proximal policy optimization / acer / ddpg / twin dueling ddpg / soft actor critic / generative adversarial imitation learning / hindsight experience replay
Stars: ✭ 2,633 (-18%)
TensorfaceThis repo is deprecated, please use Deep Video Analytics which implements face recognition using TensorFlow and Facenet.
Stars: ✭ 215 (-93.3%)
Stock PredictionStock price prediction with recurrent neural network. The data is from the Chinese stock.
Stars: ✭ 219 (-93.18%)
Python lectures파이썬Python 강의에 사용되는 소스코드Source Code와 강의 자료들을 모은 repository 입니다.
Stars: ✭ 214 (-93.34%)
TcdfTemporal Causal Discovery Framework (PyTorch): discovering causal relationships between time series
Stars: ✭ 217 (-93.24%)
Stereo TransformerOfficial Repo for Stereo Transformer: Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers.
Stars: ✭ 211 (-93.43%)
Vae ClusteringUnsupervised clustering with (Gaussian mixture) VAEs
Stars: ✭ 220 (-93.15%)
Dianjing点睛 - 头条号文章标题生成工具 (Dianjing, AI to write Title for Articles)
Stars: ✭ 214 (-93.34%)
WeightwatcherThe WeightWatcher tool for predicting the accuracy of Deep Neural Networks
Stars: ✭ 213 (-93.37%)
Bitcoin predictionThis is the code for "Bitcoin Prediction" by Siraj Raval on Youtube
Stars: ✭ 214 (-93.34%)
How To Read PytorchQuick, visual, principled introduction to pytorch code through five colab notebooks.
Stars: ✭ 218 (-93.21%)
MirrorVisualisation tool for CNNs in pytorch
Stars: ✭ 219 (-93.18%)
Python SonicProgramming Music with Python, Sonic Pi and Supercollider
Stars: ✭ 217 (-93.24%)
Skiftscikit-learn wrappers for Python fastText.
Stars: ✭ 213 (-93.37%)
Gwu data miningMaterials for GWU DNSC 6279 and DNSC 6290.
Stars: ✭ 217 (-93.24%)
Stock market predictionThis is the code for "Stock Market Prediction" by Siraj Raval on Youtube
Stars: ✭ 217 (-93.24%)
Spark Fm ParallelsgdImplementation of Factorization Machines on Spark using parallel stochastic gradient descent (python and scala)
Stars: ✭ 220 (-93.15%)
Practical 1Oxford Deep NLP 2017 course - Practical 1: word2vec
Stars: ✭ 220 (-93.15%)
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 (-93.21%)
Research Paper NotesNotes and Summaries on ML-related Research Papers (with optional implementations)
Stars: ✭ 218 (-93.21%)
TensorflowDeep Learning Zero to All - Tensorflow
Stars: ✭ 216 (-93.27%)