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Deep Learning Tuning and Visualization

Manage experiments, plot training progress, assess accuracy, explain predictions, tune training options, and visualize features learned by a network

Tune training options and improve network performance by sweeping hyperparameters or using Bayesian optimization. Use Experiment Manager to manage deep learning experiments that train networks under various initial conditions and compare the results. Monitor training progress using built-in plots of network accuracy and loss. To investigate trained networks, you can use visualization techniques such as Grad-CAM, occlusion sensitivity, LIME, and deep dream. You can also investigate network robustness using adversarial examples and test your trained network by making predictions using new data.

  • Deep Learning Tuning
    Programmatically tune training options, resume training from a checkpoint, and investigate adversarial examples
  • Deep Learning Visualization
    Plot training progress, assess accuracy, explain predictions, and visualize features learned by a network
  • Deep Learning Experiments
    Train networks under various initial conditions, interactively tune training options, and assess your results

Featured Examples