Overview
Explore the inner workings of ImageNet classifiers through the OpenAI microscope, a comprehensive database of visualizations. Delve into the process of obtaining these visualizations and gain insights into what neural networks learn. Examine optimization techniques, dataset examples, and network architecture. Investigate neuron activation patterns and understand how feature visualization contributes to interpreting deep learning models. Learn about tools like TensorFlow Lucid for creating your own visualizations and uncover the potential applications of this technology in advancing AI research and development.
Syllabus
Introduction
What do networks learn
Optimization
Dataset Examples
OpenAI Microscope
Network Architecture
Neuron Activation
Taught by
Yannic Kilcher