grseb9s Goto Github PK

crane-shapematchingpackage

crayon

A language-agnostic interface to TensorBoard

crcdocker

Source files for Docker image mort/crcdocker

crcenvi

ENI/IDL script for Image Analysis

crcpython

Image Analysis, Classification and Change Detection in Remote Sensing

createdatasets

R package for downloading and preprocessing benchmark datasets

credit-card-fraud-detection-using-autoencoders-in-keras

iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

crelu

simple experiments to reproduce the CReLU paper

crf

Conditional random fields package for images

crf-labeling

Semantic image segmentation with Conditional Random Filed and Graph Cut

crf-test

Keras CRF experiments

crfasrnn_keras

CRF-RNN Keras/Tensorflow version

crfsharp

CRFSharp is Conditional Random Fields implemented by .NET(C#), a machine learning algorithm for learning from labeled sequences of examples.

crikit2

CRIKit2 is a hyperspectral imaging toolkit formerly known as the coherent Raman imaging toolkit.

crisismappingtoolkit

NASA Ames Crisis Mapping Toolkit

crnn

Convolutional-Recursive Deep Learning for 3D Object Classification Richard Socher, Brody Huval, Bharath Bhat, Christopher D. Manning and Andrew Y. Ng Advances in Neural Information Processing Systems (NIPS 2012)

crnn.pytorch

Convolutional recurrent network in pytorch

crop-loss-epar

Building a model to detect crop loss from satellite imagery.

cropplanning

Crop Planning Software for Small Farms and Serious Gardeners

crossnet

Cross-view Supervised Learning

crossproductkernel-noisydata

Experiments using a support vector machine with kernels on fuzzy sets on noise data

cs229-machine-learning

CS229 Machine Learning open course by Andrew Ng

cs231a_project

This problem takes an input of an RGB-D image in the NYU-RGBD dataset. The goal is to perform 3D object recognition and indoor scene classification. These two tasks are tackled jointly in our holistic model, that is, some constraints are placed among scenes and these objects when reasoning about object detection, which helps boost the performance of scene classification and object detection.

cs231n

cs231n assignments

cs269-shape-matching

CS269 - Visual Modeling final project on Matching shapes and Object Recognition with Shape Contexts

cs314-2016

Lecture and Homework Codes for CS314 at Purdue

cs520-2017

Lecture and Homework Codes for CS520 at Purdue

csad

CSAD: Contrastive Structured Anomaly Detection for Gaussian Graphical Models

csci447-2015

CSCI477: Machine Learning and Soft Computing

cse250b_nn

CSE250B Project #4: Semi-Supervised Recursive Autoencoders