Skip to content
#

softmax-classifier

Here are 47 public repositories matching this topic...

Recognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.

  • Updated Jul 20, 2023
  • PHP

Classifying fruit types using a deep learning method, namely Convolutional Neural Network (CNN/ConvNet), which is a type of artificial neural network that is generally used in image recognition and processing. And carry out the process of improvement mode with transfer learning.

  • Updated Jul 30, 2022
  • Jupyter Notebook
Radiography-Based-Diagnosis-Of-COVID-19-Using-Deep-Learning

Developed a Convolutional Neural Network based on VGG16 architecture to diagnose COVID-19 and classify chest X-rays of patients suffering from COVID-19, Ground Glass Opacity and Viral Pneumonia. This repository contains the link to the dataset, python code for visualizing the obtained data and developing the model using Keras API.

  • Updated Apr 15, 2021
  • Jupyter Notebook

Neural network-based character recognition using MATLAB. The algorithm does not rely on external ML modules, and is rigorously defined from scratch. A report is included which explains the theory, algorithm performance comparisons, and hyperparameter optimization.

  • Updated Aug 19, 2021
  • MATLAB

Convolution Neural Network to predict Covid-19. This is a CNN model which predicts whether you have Healthy or you have Coronavirus or you have Pneumonia. I implemented CNN from Scratch and I implemented VGG-16 architecture. This model takes your CT scan report as input and will tell you the result. This Convolutional layer Connects to DeepNeura…

  • Updated Aug 29, 2021
  • Jupyter Notebook

Improve this page

Add a description, image, and links to the softmax-classifier topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the softmax-classifier topic, visit your repo's landing page and select "manage topics."

Learn more