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www.khanna.law | ||
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programmathically.com
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| | | | | Sharing is caringTweetIn this post, we develop an understanding of why gradients can vanish or explode when training deep neural networks. Furthermore, we look at some strategies for avoiding exploding and vanishing gradients. The vanishing gradient problem describes a situation encountered in the training of neural networks where the gradients used to update the weights [] | |
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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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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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sander.ai
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| | | I have published the code for my solution for the Galaxy Zoo Challenge on GitHub | ||
