Perceptrons (book)

Applied sciences
Information engineering
Machine learning
Artificial neural networks

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Related lectures (31)

General Introduction to Artificial Neural Networks

Covers the history and inspiration behind artificial neural networks, the structure of neurons, learning through synaptic connections, and the mathematical description of artificial neurons.

Machine Learning Fundamentals

Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.

Photonic Extreme Learning Machines: Reservoir Computing Techniques

Covers photonic extreme learning machines and reservoir computing, focusing on their architectures, programming techniques, and applications in optical computing.

Dimensionality Reduction: PCA and Autoencoders

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Multilayer Networks: First Steps

Covers the preparation for deriving the Backprop algorithm in layered networks using multi-layer perceptrons and gradient descent.

XOR Problem: Neural Networks

Delves into solving the XOR problem using a two-layer neural network.

Neural Networks: Perceptron and Backpropagation

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

Crash course on Deep Learning

Covers a crash course on deep learning, including the Mark I Perceptron, neural networks, optimization algorithms, and practical training aspects.

Neural Networks Recap: Activation Functions

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Multilayer Perceptron: Training and Backpropagation

Explores the challenges of training Multilayer Perceptrons and the backpropagation algorithm.

The visual system

Explores perceptrons in neural networks and their analogy with the visual system.

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