ecemd

ecemd's Stars

• david-kamensky/VarMINT

  Variational Multiscale Incompressible Navier--Stokes Toolkit

  Language:Python118

• barbagroup/CFDPython

  A sequence of Jupyter notebooks featuring the "12 Steps to Navier-Stokes" http://lorenabarba.com/

  Language:Jupyter Notebook3.4k1.2k

• AshutoshLembhe/Deep-Learning-for-Image-Classification

  This project has a two-deep learning methods.1) Deep learning using by CNN. 2) Deep learning using Transfer learning using Alexnet. The second method uses two types of image dataset one is of flowers and the other is of roundworms.

  Language:MATLAB51

• somdipdey/SoCodeCNN

  Main program code to convert Program Source Code (C) to SoCodeCNN Images, as explained in the paper, "SoCodeCNN: Program Source Code for Visual CNN Classification Using Computer Vision Methodology", published in IEEE Access.

  Language:MATLAB3

• sancharidan/Image-CLassification-using-CNN

  Classification of images in CIFAR 10 dataset uding a deep convolutional neural network architecture. The backpropagation formulae have been derived for the CNN and accordingly the algorithm developed. Mini batch gradient descent is used to learn the biases and filters of the network.

  Language:MATLAB53

• gabrieletiboni/Image-classification-on-Caltech101-using-CNNs

  Training of a Convolutional Neural Network for image classification on dataset Caltech-101 by using AlexNet structure with both transfer learning and not.

  Language:Jupyter Notebook72

• robertofranceschi/Image-classification-techniques

  Comparison CNNs (Alexnet, VGG-16, ResNet) for image classification on dataset Caltech-101. Transfer learning and data augmentation applied and compared results with the training from scratch.

  Language:Jupyter Notebook71

• iamruta999/EE660_project_101Caltech

  Language:Matlab1

• sruthikesh-MU/caltech101Classification

  Caltech 101 dataset classification using neural networks and image preprocessing.

  Language:Matlab21

• xixi415415/Object-recognition-Task

  deep feature extraction, data augmentation, Caltech101, Caltech256, Cifar10

  3

• Roshni1999/Analysis-Interpretation-Of-Biological-Data-BT3041

  Course Assignments

  Language:MATLAB1

• deyjishnu/digit-recognition

  The purpose of this project is to take handwritten digits as input, process the digits, train the neural network algorithm with the processed data, to recognize the pattern and successfully identify the test digits. The popular MNIST dataset is used for the training and testing purposes. The IDE used is MATLAB

  Language:MATLAB333

• BinhMisfit/pet-recognition

  A sample code for cat/dog recognition

  Language:Matlab1

• AsadAzam/HandwrittenTextRecognition

  Language:MATLAB2

• lhoang29/DigitRecognition

  Digit recognition using deep learning (convolutional neural networks)

  Language:Matlab125

• shivang8/Digit-Recognition

  Digit Recognition using backpropagation algorithm on Artificial Neural Network with MATLAB. Dataset used from MNSIT.

  Language:Matlab63

• johnnyxcy/HandwritingRecognition

  Language:MATLAB2

• DipankerSingh/Multi_Class_Classification_For_RecognizingHandWrittenDigits

  Implemented one-vs-all logistic regression to recognize hand-written digits

  Language:Matlab1