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Related lectures (31)

Page 3 of 4

Threat Modeling: Adversarial Examples and Backdoors

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

Relation of SARSA and Bellman equation

Explores the relation between fluctuating Q-values in SARSA and the Bellman equation through expectations and policy constancy.

Convolutional Neural Networks

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

Structures in Non-Convex Optimization

Explores non-convex optimization in deep learning, covering critical points, SGD convergence, saddle points, and adaptive gradient methods.

Gradient Descent: Principles and Applications

Covers gradient descent, its principles, applications, and convergence rates in optimization for machine learning.

Neural Networks: Multi-layers

Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.

Neural Network Training

Covers the training process of a neural network, including feedforward, cost function, gradient checking, and visualization of hidden layers.

Adaptive Gradient Methods: Theory and Applications

Explores adaptive gradient methods, their properties, convergence, and comparison with traditional optimization algorithms.

Optimizing Loss Functions: Gradient Descent Variants

Explores early stopping in gradient descent and variants for faster optimization.

Neural Networks: Perceptron and Backpropagation

Covers the basics of neural networks, including the perceptron model and backpropagation.