New Feature: Added new interface skater.core.local_interpretation.dnni.deep_interpreter.DeepInterpreter for interpreting tensorflow and Keras based models enabling support for interpreting DNNs using gradient-based e-LRP and Integrated Gradient through DeepInterpreter.explain added support to visualize relevance/attribution scores for interpreting image and text inputs skater.core.visualizer.image_relevance_visualizer.visualize skater.core.visualizer.text_relevance_visualizer import build_visual_explainer, show_in_notebook user-friendly Utility functions to generate simple yet effective conditional adversarial examples for image inputs skater.util.image_ops import load_image, show_image, normalize, add_noise, flip_pixels, image_transformation skater.util.image_ops import in_between, greater_than, greater_than_or_equal More interactive notebook use-cases for building and interpreting DNNs for evaluating model stability/identifying blind spots Updates to documentation, https://datascienceinc.github.io/Skater/overview.html New section summarizing Notebook examples https://datascienceinc.github.io/Skater/gallery.html Other bug fixes Credits: Special thanks to Marco Ancona(@marcoancona) for guiding in enabling this feature within Skater. Thanks to all other contributors for helping move the library forward every day.