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Related lectures (32)

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Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Neural Networks: Perceptron Model and Backpropagation Algorithm

Covers the perceptron model and backpropagation algorithm in neural 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.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Statistical Physics in Machine Learning: Understanding Deep Learning

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.

Deep Learning Fundamentals

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Mathematics of Data: From Theory to Computation

Covers key concepts in data mathematics, including automatic differentiation, linear layers, and attention layers.

Machine Learning for Feature Extraction

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

Understanding Deep Learning

Explores deep learning fundamentals, including image classification, neural network working principles, and machine learning challenges.