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 charleslabs.fr
Charles' Labs - AI-powered digital signal processing
| | matt.might.net
Hello, Perceptron: An introduction to artificial neural networks
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| | [AI summary] This text explains how a single perceptron can learn basic Boolean functions like AND, OR, and NOT, but fails to learn the non-linearly separable XOR function. This limitation led to the development of modern artificial neural networks (ANNs). The transition from single perceptrons to ANNs involves three key changes: 1) Adding multiple layers of perceptrons to create Multilayer Perceptron (MLP) networks, enabling modeling of complex non-linear relationships. 2) Introducing non-linear activation functions like sigmoid, tanh, and ReLU to allow networks to learn non-linear functions. 3) Implementing backpropagation and gradient descent algorithms for efficient training of multilayer networks. These changes allow ANNs to overcome the limitations of ...
| | vankessel.io
Introduction to Neural Networks | Kara van Kessel
1.2 parsecs away

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| | A blog for my thoughts. Mostly philosophy, math, and programming.
| | blog.ephorie.de
Logistic Regression as the Smallest Possible Neural Network - Learning Machines
1.7 parsecs away

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| | [AI summary] The blog post explores the connection between logistic regression and neural networks, demonstrating how logistic regression can be viewed as the simplest form of a neural network through mathematical equivalence and practical examples.
| | research.google
Transformer: A Novel Neural Network Architecture for Language Understanding
7.6 parsecs away

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| Posted by Jakob Uszkoreit, Software Engineer, Natural Language Understanding Neural networks, in particular recurrent neural networks (RNNs), are n...