Perceptron

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Related lectures (31)

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Neural Networks: Perceptron Model and Backpropagation Algorithm

Covers the perceptron model and backpropagation algorithm in neural networks.

Scientific Machine Learning: Optimal Errors

Explores optimal errors in high-dimensional models, comparing algorithms and shedding light on the interplay between model architecture and performance.

Introduction to the Perceptron Algorithm

Introduces the perceptron algorithm and its geometric interpretation, emphasizing the rotation of the hyperplane due to misclassified patterns.

Introduction to Supervised Learning: Classification and Perceptrons

Explores supervised learning through classification as a geometric problem and the concept of finding a separating surface.

Neural Networks: Regression and Classification

Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Computer Vision History Recap

Offers a historical overview of computer vision, exploring key developments and influential figures in the field.

Neural Taskonomy and Historical Perspectives in Visual Intelligence

Covers Neural Taskonomy, the evolution of neural networks, and historical perspectives in visual intelligence.

Neural Networks: Basics and Applications

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

Neural Networks: Perceptron

Covers the main concepts of neural networks, including the Perceptron model and training algorithms.

Neural Networks: Basics and History

Covers the history and fundamental concepts of neural networks, including the mathematical model of a neuron, gradient descent, and the multilayer perceptron.