Уровень 0 · материалов: 4
В кластер входят руководства по программной реализации многослойных или однослойных перцептронов на языках C#, C или C++, и не входят общие теоретические статьи о машинном обучении без привязки к коду на этих языках.
Общие признаки: создание нейронных сетей с нуля, алгоритм обратного распространения ошибки, использование C#, C++ или C, отказ от внешних ML-библиотек
Группа выше: Основы машинного обучения и нейросетей
Смысл: The text serves as an educational tutorial demonstrating that a functional multilayer perceptron can be built using basic C/C++ without external libraries, highlighting the fundamental logic of feed-forward and backpropagation algorithms.
A technical guide providing a library-free C/C++ implementation of a multilayer perceptron with code examples and performance results on the MNIST dataset.
Смысл: The text serves as a tutorial to explain the theoretical mechanics of the backpropagation algorithm and demonstrate how to implement it in C++ using professional software design patterns.
A detailed guide covering the theory and C++ implementation of the backpropagation algorithm for multi-layer neural networks.
Смысл: The text is a technical guide on how to build a multi-layer perceptron from scratch using C#, focusing on solving basic logic problems (XOR/XAND) to demonstrate the principles of backpropagation and weight adjustment without relying on external ML frameworks.
A technical tutorial demonstrating how to implement a multi-layer perceptron for logic gate problems using C# without external ML libraries.
Смысл: The main idea is to demystify the implementation of the Rosenblatt Perceptron by providing a simplified C# architectural approach, emphasizing that its error-correction learning is robust and doesn't require complex mathematical formulas to begin training from zero weights.
A C#-based conceptual guide explaining the structure and error-correction learning process of a Rosenblatt Perceptron.