Getting Started Image Classifier for MNIST and CIFAR-10

Added Jun 2025 3 design docs

The gap between reading about neural networks and having trained one yourself is the single biggest hurdle for machine learning beginners, and it is best crossed on well-understood benchmark datasets where results are easy to interpret. MNIST digits and CIFAR-10 photographs offer two well-studied difficulty levels: one nearly always works, the other exposes the limits of a simple model. The intern builds a beginner-friendly classification project in TensorFlow covering both datasets, then deploys it as a Streamlit web application. The workflow includes loading and normalizing the data, defining and training a compact neural network for each dataset, and evaluating accuracy on held-out test images. In the app, users upload their own images or pick samples, and the classifier returns predicted labels with confidence scores, with a simple comparison of how the model performs on clean digits versus noisier color photographs of objects and animals. Completing the project gives the intern their first full training-to-deployment loop: preparing data, fitting a model, measuring honest performance, and serving predictions through a Python web interface. It establishes the vocabulary and habits, train and test discipline, evaluation before celebration, that every later computer vision project builds upon.

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