Reinforcement Learning Fundamentals

Related lectures (31)

Page 3 of 4

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

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

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Explores deep neural network training, optimization, preventing overfitting, and different network architectures.

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

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Explores the Random Feature Method for solving PDEs using machine learning algorithms to approximate high-dimensional functions efficiently.

Covers the importance of preventive maintenance for pavement distress detection and introduces machine learning concepts for engineers.