Learning Rate and Batch Size Effects Visualizer for Deep Learning

Added Jun 2025 3 design docs

Ask a beginner why their network is not learning and the answer is usually hiding in two numbers: the learning rate and the batch size. Their effects are far easier to understand seen than described, and this project builds the tool that shows them clearly. The intern creates a visualizer, developed in Jupyter with Keras and TensorFlow, that trains a small neural network on a compact dataset repeatedly under different learning rates and batch sizes, then plots the resulting training curves together for comparison. The characteristic patterns become recognizable at a glance: divergence when the rate is too high, glacial progress when it is too low, the noisy-but-fast trajectories of small batches versus the smooth ones of large batches. Users can adjust the settings and re-run experiments quickly enough to explore the space themselves and form their own rules of thumb. The project builds diagnostic instinct — the ability to look at a loss curve and name the likely cause — which is among the most practical skills in deep learning. The intern also practices designing controlled experiments and communicating their results visually, habits that transfer directly to research and production work.

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