Multivariate Regression and Classification Using a Feed-Forward Neural Network and Gradient Descent Optimization.
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Updated
Oct 27, 2021 - Python
Multivariate Regression and Classification Using a Feed-Forward Neural Network and Gradient Descent Optimization.
[RU] Обучение многослойного перцептрона с одним скрытым слоем методом обратного распространения ошибки. [EN] Training of a multilayer perceptron with one hidden layer by the back-propagating errors method.
Implementing the backpropagation algorithm for Neural Networks
A backpropagation algorithm is implemented in Python from scratch to perform a classification analysis.
Implementation of the back-propagation algorithm using only the linear algebra and other mathematics tool available in numpy and scipy.
I made a Multi Layer Perceptron(Neural Network) in numpy
Python implementation of the backpropagation algorithm.
Using only numpy in Python, a neural network with a forward and backward method is used to classify given points (x1, x2) to a color of red or blue.
Demonstration of the mini-lab (practical) component activities conducted for the course of Neural Networks and Deep Learning (19CSE456).
Neural Network implementation with Numpy
2nd Project of Course 'Machine Learning' of the SMARTNET programme. Taken at the National and Kapodistrian University of Athens.
Automatic backpropagation implemented in numpy,
Auto Propagation System for Complex Neural Networks of Luma Python library
Simple Multi-Layer Perceptron(MLP) with forward and backward propagation
Neural network/Back Propagation implemented from scratch for MNIST.从零开始实现神经网络和反向传播算法,识别MNIST
Generates an neural network by using the back propagation algorithm. Accuracy testing included.
Mapping Spike Activities with Multiplicity, Adaptability, and Plasticity into Bio-Plausible Spiking Neural Networks
A Numpy based implementation to understand the backpropagation algorithm using the XOR Problem.
MNIST Classification using Neural Network and Back Propagation. Written in Python and depends only on Numpy
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