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andrewpwheeler.com | ||
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gregorygundersen.com
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| | | | | Gregory Gundersen is a quantitative researcher in New York. | |
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www.markhw.com
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| | | | | [AI summary] The blog post discusses modeling variance in data using the gamlss package in R, focusing on the user's film ratings over time. It highlights how the standard deviation of ratings increases with the release year of films, reflecting the user's movie selection habits. The analysis shows that older films have higher average ratings and lower variability, while newer films have lower average ratings and higher variability. The post emphasizes the importance of considering variance in social phenomena and provides practical examples using R for data visualization and statistical modeling. | |
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www.chrisritchie.org
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| | | | | [AI summary] The article discusses the application of neural networks to stock market prediction, explaining how they assign dynamic weights to various inputs like historical prices, expert opinions, and internet usage to improve forecasting accuracy. | |
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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 ... | ||
