Deep Learning for Autonomous Vehicles

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Related lectures (30)

Page 3 of 3

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

Threat Modeling: Adversarial Examples and Backdoors

Covers threat modeling in deep learning for autonomous vehicles, focusing on defending against adversarial examples and backdoors.

Acquiring Data for Learning

Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.

Intelligence Systems: Forecasting and Action

Delves into developing intelligence systems for predicting human behavior and enhancing AI-driven technologies' safety and efficiency.

Multilayer Neural Networks: Deep Learning

Covers the fundamentals of multilayer neural networks and deep learning.

Reinforcement Learning: Basics and Applications

Covers the basics of reinforcement learning, including trial-and-error learning, Q-learning, deep RL, and applications in gaming and planning.

Machine Learning for Feature Extraction

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Deep Visual Recognition: Interpretability

Explores deep visual recognition, interpretability, CNN architectures, visual dictionaries, and attention mechanisms.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Machine Learning Fundamentals

Covers fundamental principles, opportunities, and challenges in machine learning.