一文彻底搞懂BP算法:原理推导+数据演示+项目实战
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Updated
Jul 2, 2019 - Python
一文彻底搞懂BP算法:原理推导+数据演示+项目实战
Training spiking networks with hybrid ann-snn conversion and spike-based backpropagation
Back Propagation, Python
Minimalist deep learning library with first and second-order optimization algorithms made for educational purpose
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
Mapping Spike Activities with Multiplicity, Adaptability, and Plasticity into Bio-Plausible Spiking Neural Networks
MNIST Classification using Neural Network and Back Propagation. Written in Python and depends only on Numpy
Implementation of different neural networks with back-propagation logic.
Implementation of the back-propagation algorithm using only the linear algebra and other mathematics tool available in numpy and scipy.
Demonstration of the mini-lab (practical) component activities conducted for the course of Neural Networks and Deep Learning (19CSE456).
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.
Multivariate Regression and Classification Using a Feed-Forward Neural Network and Gradient Descent Optimization.
Auto Propagation System for Complex Neural Networks of Luma Python library
Neural Network implementation with Numpy
Simple Backpropagation for Customer Care. Demonstrates how backpropagation can be used to train a basic neural network for customer care response prediction.
Automatic backpropagation implemented in numpy,
Implementing the backpropagation algorithm for Neural Networks
[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.
Multi-Layer Perceptron Implementation
A mini-Library consisting of the implementation of classes/functions useful for Machine Learning Applications.
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