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rkoutnik.com | ||
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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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zserge.com
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| | | | | Neural network and deep learning introduction for those who skipped the math class but wants to follow the trend | |
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www.3blue1brown.com
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| | | | | An overview of what a neural network is, introduced in the context of recognizing hand-written digits. | |
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questionableengineering.com
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| | | John W Grun AbstractIn this paper, a manually implemented LeNet-5 convolutional NN with an Adam optimizer written in Numpy will be presented. This paper will also cover a description of the data use | ||
