Lectures related to ImageNet | EPFL Graph Search

Covers the No Free Lunch Theorem and the role of inductive bias in deep learning and reinforcement learning.

Introduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Explores the evaluation of machine and human accuracy and robustness on ImageNet, highlighting progress, challenges, and the need for improvement.

Delves into graph search, neural networks, and deep learning, covering topics like convolutional neural networks and artificial neural networks.

Explores segmentation techniques, including CNNs and U-Net models, for image recognition and analysis, emphasizing time-saving automated methods.