labrijisaad / Mnist-Deep-Learning-Project

his repository is dedicated to tackling three distinct problems using deep learning techniques on the Mnist dataset.

MNIST Deep Learning Project

Overview 🚀

This repository explores the application of three distinct techniques using the MNIST dataset. Our primary objective is to assess the efficacy of supervised, semi-supervised, and self-supervised learning methods.

Techniques 🎯

1. Supervised Learning

   • Objective: Train a model to recognize numbers using 100 MNIST examples.
   • Approach: Employ various methodologies to optimize model performance and utilize data augmentation to enhance the model's accuracy.

2. Semi-Supervised Learning

   • Objective: Enable the model to autonomously comprehend data without labeled guidance.
   • Task: Classify unlabeled MNIST data into distinct number groups and reconstruct the 10 classes of the target (label).

3. Self-Supervised Learning

   • Objective: Evaluate the independent performance of trained models.
   • Significance: Assess the model's ability to make accurate predictions without external guidance.

📋 Project Deliverables

• Supervised Learning: Present the model's proficiency in recognizing numbers.
• Semi-Supervised Learning: Demonstrate the model's autonomous categorization of unlabeled data.
• Self-Supervised Learning: Evaluate the model's capability to operate independently.