Find optimal training options for neural networks by sweeping through a range of
hyperparameter values or using Bayesian optimization. Use the built-in function
trainNetwork or define your own custom training function.
Test different training configurations at the same time by running your experiment
in parallel. Monitor your progress by using training plots. Use confusion matrices
and custom metric functions to evaluate your trained network. Refine your
experiments by sorting and filtering. Use annotations to record your
|Experiment Manager||Design and run experiments to train and compare deep learning networks|
|Update results table and training plots for custom training experiments|
Train a deep learning network for classification using Experiment Manager.
Train a deep learning network for regression using Experiment Manager.
Train deep networks in parallel using Experiment Manager.
Use metric functions to evaluate the results of an experiment.
Find optimal network hyperparameters and training options for convolutional neural networks.
Use Experiment Manager to tune the hyperparameters of a network trained in Deep Network Designer.