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• Multiple convolutional layers
• Pooling layers for feature extraction
• Fully connected classification layers
• Model training with optimization
• Accuracy evaluation metrics
• Loss function monitoring
• High accuracy recognition
• Real-time prediction
• Image preprocessing
• Python - Programming language
• MNIST - Training dataset
• NumPy - Numerical computing
Case Study · DL
Handwritten Digit Recognition - Deep Learning Model using Pytorch.
A CNN-based deep learning model for classifying handwritten digits from the MNIST dataset with high accuracy.
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Case Study
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Reach out for demo
Project Overview
This project involves developing and evaluating a Convolutional Neural Network (CNN) model for classifying handwritten digits from the well-known MNIST dataset. The CNN is trained to accurately recognize and classify digits 0-9, showcasing deep learning capabilities in image classification.
🚀 Features
Deep learning for handwritten digit recognition.
🧠 Model Architecture
• Convolutional Neural Network (CNN)• Multiple convolutional layers
• Pooling layers for feature extraction
• Fully connected classification layers
📊 Training & Evaluation
• MNIST dataset (60,000 training images)• Model training with optimization
• Accuracy evaluation metrics
• Loss function monitoring
🎯 Capabilities
• Digit classification (0-9)• High accuracy recognition
• Real-time prediction
• Image preprocessing
🛠️ Tech Stack
• PyTorch - Deep learning framework• Python - Programming language
• MNIST - Training dataset
• NumPy - Numerical computing
Technologies & Tools
Deep Learning Model using Pytorch