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Handwritten Digit Recognition Demo with LeNet-5

Added Jun 2025 3 design docs

Handwritten digit recognition on the MNIST dataset is the canonical entry point to deep learning, and the LeNet-5 architecture that solves it is small enough for a beginner to understand layer by layer while still embodying every core idea of modern convolutional networks. Building it end to end is how the concepts of convolution, pooling, and dense classification stop being vocabulary and become intuition.

The intern implements the LeNet-5 network in TensorFlow, trains it on MNIST, and wraps the result in an interactive Streamlit application. Users submit digit images and receive instant predictions with per-class confidence scores, and the intern builds visualization panels that expose the model's inner workings: the architecture's layer structure, training and validation accuracy curves, and examples the network gets wrong. Handling input images so they match the format the network was trained on gives the intern their first real taste of preprocessing discipline.

By completing the demo, the intern gains a concrete understanding of how a convolutional network is assembled, trained, and evaluated, learns to communicate model behavior visually, and produces a clean, interactive Python project that demonstrates genuine comprehension of deep learning fundamentals rather than copy-pasted code.

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