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Completeddeep-learning
Handwritten Digit Recognition
CNN-based system for classifying handwritten digits using MNIST dataset
June 2025 – July 2025Deep Learning Computer Vision
Tech Stack
PythonTensorFlowPyTorchCNNNumPy
What I Built
- 1Designed and trained a Convolutional Neural Network (CNN) for digit classification
- 2Used MNIST dataset (70,000 images) for training and validation
- 3Achieved high classification accuracy through model optimization
- 4Visualized training performance and prediction results
Key Metrics
MNIST (70K images)
dataset
CNN
architecture
High
accuracy
Problem Context
This project addressed deep learning computer vision challenges. The goal was to build a robust system that could handle real-world data and produce actionable insights for decision-making.
Architecture & Approach
┌─────────────────────────────────────────────────────────┐ │ Data Pipeline │ ├─────────────────────────────────────────────────────────┤ │ │ │ Raw Data ──▶ Preprocessing ──▶ Feature Engineering │ │ │ │ │ │ ▼ ▼ │ │ Data Cleaning Feature Selection │ │ │ │ │ │ └────────┬───────────┘ │ │ │ │ │ ▼ │ │ Model Training │ │ │ │ │ ▼ │ │ Evaluation & Tuning │ │ │ │ │ ▼ │ │ Final Predictions │ │ │ └─────────────────────────────────────────────────────────┘
Key Lessons
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Feature engineering has outsized impact on model performance. Domain knowledge matters more than model complexity.
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Cross-validation is essential for reliable evaluation. Single train-test splits can be misleading.
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Start simple (Linear/Logistic Regression) before complex models. Baselines provide crucial context.