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www.globalnerdy.com | ||
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futurism.com
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| | | | | A team of scientists uncovered how our brains process 3D images - then discovered that neural networks happen to do it the same way. | |
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www.joeydevilla.com
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| | | | | Not that long ago, they'd cry, moan, gnash their teeth and whine "Nobody wants to work!" Now, they're still crying, moaning, and gnashing their teeth, but the tune has changed: "Nobody's qualified to work!" What they're really complaining about is that nobody wants to work for at the level they want for the pay they're [...] | |
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www.chrisritchie.org
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| | | | | [AI summary] The post documents a web-based simulation of artificial life agents controlled by evolving neural networks that learn to eat, swarm, and reproduce within a basic environment. | |
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matt.might.net
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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 ... | ||
