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Differentiable Image Parameterizations
| | www.asimovinstitute.org
The Neural Network Zoo - The Asimov Institute
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| | With new neural networkarchitectures popping up every now and then, its hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containingmany of thosearchitectures. Most of theseare neural networks, some are completely []
| | blog.otoro.net
Generating Abstract Patterns with TensorFlow |
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| | A Diamond is Forever (2016)Web Demo
| | blog.otoro.net
Neural Network Evolution Playground with Backprop NEAT |
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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 ...
| | www.paepper.com
Training a neural network with Numpy :: Päpper's Machine Learning Blog - This blog features state of the art applications in ...
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| [AI summary] This article explains how to train a simple neural network using Numpy in Python without relying on frameworks like TensorFlow or PyTorch, focusing on the implementation of ReLU activation, weight initialization, and gradient descent for optimization.