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dennybritz.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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www.analyticsvidhya.com
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| | | | | Explore RNNs: their unique architecture, working principles, BPTT, pros/cons, and Python implementation using Keras. | |
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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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explog.in
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| | | [AI summary] The user has shared a detailed implementation of a single-layer neural network in Rust, along with its training and evaluation process. They also provided the Cargo.toml file for the project and mentioned the results of running the code. The user is seeking feedback, comments, or suggestions for improvement, and they have included a note about the history of the project. | ||
