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Structuring Deep Learning Projects
| | michael-lewis.com
A machine learning glossary for hackers ยท Michael I Lewis
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| | This is a short summary of some of the terminology used in machine learning, with an emphasis on neural networks. I've put it together primarily to help my own understanding, phrasing it largely in non-mathematical terms. As such it may be of use to others who come from more of a programming than a mathematical background.
| | blog.ephorie.de
Logistic Regression as the Smallest Possible Neural Network - Learning Machines
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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.
| | 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 ...
| | 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.