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mccormickml.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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thenumb.at
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| | | | | [AI summary] The text provides an in-depth overview of various neural field techniques, focusing on their applications in representing images, geometry, and light fields. It discusses methods such as positional encoding, hash encoding, and neural SDFs, highlighting their advantages in terms of model size, training efficiency, and quality of representation. The text also touches on the broader implications of these techniques in fields like computer vision and real-time rendering, emphasizing their potential to revolutionize how we model and interact with digital content. | |
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kavita-ganesan.com
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| | | This article examines the parts that make up neural networks and deep neural networks, as well as the fundamental different types of models (e.g. regression), their constituent parts (and how they contribute to model accuracy), and which tasks they are designed to learn. | ||
