/explore
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danielegrattarola.github.io | ||
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blogs.mathworks.com
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| | | | | This blog gives you an overview of physics-informed machine learning: what it's used for, what we mean by physics knowledge and how it informs AI methods. | |
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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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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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explog.in
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| | | [AI summary] The user has shared a detailed implementation of a single-layer neural network in Rust, along with its training and evaluation process. They also provided the Cargo.toml file for the project and mentioned the results of running the code. The user is seeking feedback, comments, or suggestions for improvement, and they have included a note about the history of the project. | ||
