@lerabyte: The Thought Process of a Data Scientist 👩‍💻 I didn’t start by picking a neural network. I started by evaluating the data and the pipeline. I explored multiple datasets, including the classic Kaggle Cats vs Dogs set, but the moment I realized how much of the “modeling” would turn into filename parsing, manual split logic, and opportunities for silent bugs, I switched to TensorFlow Datasets and used the Oxford-IIIT Pet dataset for its structured labels and reproducible setup. From there, the architecture choices were about alignment, not complexity: a pretrained MobileNetV2 backbone to reuse strong visual features, a lightweight classification head to adapt those features to a simple cat vs dog decision, and a two-stage training process where we first freeze the backbone to stabilize learning, then fine-tune the top layers with a smaller learning rate to improve performance without destroying what the model already knows. And instead of relying on accuracy alone, I tracked metrics that answer different questions about behavior and tradeoffs because the goal isn’t just a high score, it’s a model you can actually understand and trust. Full code: https://tinyurl.com/CNNAnimals #ml #machinelearning #tensorflow #python #datascience

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Sunday 01 March 2026 02:40:33 GMT