GitHub

Using LDA, SVM, and Decision Trees to deal with MNIST data set

This project involves the classification of handwritten digits using three different classifiers: Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Decision Trees. The goal is to ...
Frontiers

Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning

Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we ...
Visual Studio Magazine

Convolutional Neural Networks for MNIST Data Using PyTorch

Dr. James McCaffrey of Microsoft Research details the "Hello World" of image classification: a convolutional neural network (CNN) applied to the MNIST digits dataset. The "Hello World" of image ...
Frontiers

Memristor Based Binary Convolutional Neural Network Architecture With Configurable Neurons

The memristor-based convolutional neural network (CNN) gives full play to the advantages of memristive devices, such as low power consumption, high integration density, and strong network recognition ...
GitHub

Model Zoo

Check out the model zoo documentation for details. 16-layer: 7.5% top-5 error on ILSVRC-2012-val, 7.4% top-5 error on ILSVRC-2012-test 19-layer: 7.5% top-5 error on ...
Analytics India Magazine

6 MNIST Image Datasets That Data Scientists Should Be Aware Of (With Python Implementation)

In today’s article, we’ll be talking about the very basic and primarily the most curated datasets used for deep learning in computer vision.To show the performance of these neural networks some basic ...
Scientific Research Publishing

Creating a Dataset to Boost Civil Engineering Deep Learning Research and Application ()

1 Graduate Transportation Associate, Tennessee Department of Transportation, Nashville, Tennessee, USA. 2 University of Tennessee at Chattanooga, Chattanooga, Tennessee, USA. 3 Transportation Engineer ...