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www.khanna.law | ||
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michael-lewis.com
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| | | | | This is a short summary of some of the terminology used in machine learning, with an emphasis on neural networks. I've put it together primarily to help my own understanding, phrasing it largely in non-mathematical terms. As such it may be of use to others who come from more of a programming than a mathematical background. | |
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blog.otoro.net
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| | | | | [AI summary] This article describes a project that combines genetic algorithms, NEAT (NeuroEvolution of Augmenting Topologies), and backpropagation to evolve neural networks for classification tasks. The key components include: 1) Using NEAT to evolve neural networks with various activation functions, 2) Applying backpropagation to optimize the weights of these networks, and 3) Visualizing the results of the evolved networks on different datasets (e.g., XOR, two circles, spiral). The project also includes a web-based demo where users can interact with the system, adjust parameters, and observe the evolution process. The author explores how the genetic algorithm can discover useful features (like squaring inputs) without human intervention, and discusses the ... | |
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blog.ephorie.de
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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. | |
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kevinlynagh.com
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| | | [AI summary] The author discusses their experience developing a simple neural network for sensor data processing on a microcontroller, highlighting challenges with quantization and inference optimization. | ||
