https://github.com/mehassanhmood/multiclass-classification

Using Dense Neural Networks on MNIST Datasets
https://github.com/mehassanhmood/multiclass-classification

classification deep-learning supervised-learning unit-testing

Last synced: 13 days ago
JSON representation

Using Dense Neural Networks on MNIST Datasets

• Host: GitHub
• URL: https://github.com/mehassanhmood/multiclass-classification
• Owner: mehassanhmood
• Created: 2024-01-29T03:23:42.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-17T09:37:02.000Z (11 months ago)
• Last Synced: 2024-12-05T16:18:20.045Z (2 months ago)
• Topics: classification, deep-learning, supervised-learning, unit-testing
• Language: Python
• Homepage:
• Size: 140 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
Multiclass Classifier

## Overview

implementation of  a multiclass classifier using the concepts of deep learning.

## Objectives

1. Enhance understanding of multiclass classification.

3. Implement and train a multiclass classifier model.

4. Evaluate the performance of the model.

---

## Validating and Evaluating Your Results

### Online

1. After committing and pushing your code, check the mark on the top line (near the commit ID).

2. If some tests are failing, click on the ❌ to open up a popup, which will show details about the errors.

3. You can click the [Details]() link to see what went wrong. Pay special attention to lines with the words "Failed" or "error".

![screnshot](images/details_screenshot.png)

4. Near the bottom of the [Details]() page, you can see your score. Here are examples of 0/5 and 5/5:

![score](images/score.png) ![success](images/success.png)

5. When you achieve a perfect score, you will see a green checkmark near the commit ID.

![green](images/green.png)

### Locally

1. You can test your code locally by installing and running `pytest` (`pip install pytest` or `conda install pytest`).

2. Run the tests using the command `pytest` in your terminal. This will show the status of each test and any errors that occurred.

Good luck!