Backpropagation is a method of the gradient computation, often used in artificial neural networks to perform gradient descent.
I am trying to implement neural network with RELU. input layer -> 1 hidden layer -> relu -> …
neural-network backpropagationWe are writing a small ANN which is supposed to categorize 7000 products into 7 classes based on 10 input variables. In order …
validation machine-learning neural-network backpropagationWhat is the difference between back-propagation and feed-forward neural networks? By googling and reading, I found that in feed-forward there …
machine-learning neural-network classification backpropagationAlthough both of the above methods provide a better score for the better closeness of prediction, still cross-entropy is preferred. …
machine-learning neural-network backpropagation mean-square-error cross-entropyFollowing the example from: https://github.com/jcjohnson/pytorch-examples This code trains successfully: # Code in file tensor/two_layer_net_…
python machine-learning pytorch backpropagationI am trying to understand backpropagation in a simple 3 layered neural network with MNIST. There is the input layer with …
python numpy neural-network backpropagation softmaxI'm trying to implement a feed-forward neural network in Java. I've created three classes NNeuron, NLayer and NNetwork. The "simple" …
java neural-network backpropagation feed-forwardI am computing the backpropagation algorithm for a sparse autoencoder. I have implemented it in python using numpy and in …
python performance matlab numpy backpropagationWhat is the meaning of forward pass and backward pass in neural networks? Everybody is mentioning these expressions when talking …
neural-network backpropagation conv-neural-networkI have programmed a Neural Network in Java and am now working on the back-propagation algorithm. I've read that batch …
neural-network backpropagation