Convolutional Neural Networks with Heterogeneous Metadata
Updates: 2020/02/21: Add pixel-to-graphIn autonomous driving, convolutional neural networks are the go-to tool for various perception tasks. Although CNNs are great at distilling information from camera images (or a sequence of them in form of a video clip), I constantly bump into all kinds of metadata that do not lend themselves to convolutional neural networks.
Optuna - A hyperparameter optimization framework
A simple optimization problem: Define objective function to be optimized. Let's minimize (x - 2)^2 Suggest hyperparameter values using trial object. Here, a float value of x is suggested from -10 to 10 Create a study object and invoke the optimize method over 100 trials import optuna def objective(trial): x = trial.suggest_float('x', -10, 10) return (x - 2) ** 2 study = optuna.create_study() study
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