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 programminghistorian.org
Creating Deep Convolutional Neural Networks for Image Classification | Programming Historian
| | kavita-ganesan.com
A Gentle Introduction to Deep Neural Networks with Python - Kavita Ganesan, PhD
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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.
| | natureofcode.com
10. Neural Networks / Nature of Code
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| | I began with inanimate objects living in a world of forces, and I gave them desires, autonomy, and the ability to take action according to a system of
| | neuralnetworksanddeeplearning.com
Neural networks and deep learning
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| | [AI summary] The text provides an in-depth explanation of the backpropagation algorithm in neural networks. It starts by discussing the concept of how small changes in weights propagate through the network to affect the final cost, leading to the derivation of the partial derivatives required for gradient descent. The explanation includes a heuristic argument based on tracking the perturbation of weights through the network, resulting in a chain of partial derivatives. The text also touches on the historical context of how backpropagation was discovered, emphasizing the process of simplifying complex proofs and the role of using weighted inputs (z-values) as intermediate variables to streamline the derivation. Finally, it concludes with a citation and licens...
| | 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 ...