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Deep Learning: Fundamentals

Covers the fundamentals of deep learning, from artificial neurons to modern networks.

Machine Learning for Solving PDEs: Random Feature Method

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

Machine Learning for Physicists/Chemists: Image Classification

Covers the fundamentals of machine learning for physicists and chemists, focusing on image classification tasks using artificial intelligence.

Mathematics Special Course: Introduction and Program

Introduces the Mathematics Special Course at EPFL, emphasizing its role as a bridge to Bachelor studies and the importance of mathematics in engineering.

Deep Learning Building Blocks

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.

Artificial Intelligence: The New Radical Addition to Work

Explores the convergence of IoT, Big Data, and Deep Learning in Artificial Intelligence applications across various industries.

Deep Learning: Edge Detection and Neural Networks

Discusses edge detection techniques and the evolution of deep learning in neural networks.

Convolutional Neural Networks

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