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www.marekrei.com | ||
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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 ... | |
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futurism.com
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| | | | | This post was originally written by Manan Shah as a response to a question on Quora. | |
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blog.google
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| | | | | Neural networks can train computers to learn in a way similar to humans. Googler Maithra Raghu explains how they work. | |
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www.laptopmag.com
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| | | MIT finds AI tools like ChatGPT can make you think less, remember less, and care less - and we've already seen the effects | ||
