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thedarkside.frantzmiccoli.com | ||
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datadan.io
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| | | | | Linear regression and gradient descent are techniques that form the basis of many other, more complicated, ML/AI techniques (e.g., deep learning models). They are, thus, building blocks that all ML/AI engineers need to understand. | |
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charleslabs.fr
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| | | | | Apply complex mathematical operations with machine learning in digital signal processing. Check out two artificial neural network experiments here. | |
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bdtechtalks.com
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| | | | | Gradient descent is the main technique for training machine learning and deep learning models. Read all about it. | |
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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 ... | ||
