Network structure inspired by simplified models of biological neurons (brain cells).
I am looking for an open source neural network library. So far, I have looked at FANN, WEKA, and OpenNN. …
machine-learning artificial-intelligence neural-networkDrop-Out is regularization techniques. And I want to apply it to notMNIST data to reduce over-fitting to finish my Udacity …
neural-network tensorflow deep-learningIn Keras, we can return the output of model.fit to a history as follows: history = model.fit(X_train, …
python machine-learning neural-network deep-learning kerasWhen you run a Keras neural network model you might see something like this in the console: Epoch 1/3 6/1000 [..............................] - ETA: 7994…
python logging machine-learning neural-network kerasI am trying to implement neural network with RELU. input layer -> 1 hidden layer -> relu -> …
neural-network backpropagationBy using pyTorch there is two ways to dropout torch.nn.Dropout and torch.nn.functional.Dropout. I struggle to …
neural-network deep-learning pytorch dropoutCan someone please explain this? I know bidirectional LSTMs have a forward and backward pass but what is the advantage …
machine-learning neural-network keras lstm recurrent-neural-networkIf we have 10 eigenvectors then we can have 10 neural nodes in input layer.If we have 5 output classes then we …
machine-learning neural-network deep-learning perceptronClassification problems, such as logistic regression or multinomial logistic regression, optimize a cross-entropy loss. Normally, the cross-entropy layer follows the …
python tensorflow neural-network logistic-regression cross-entropyCurrently I use the following code: callbacks = [ EarlyStopping(monitor='val_loss', patience=2, verbose=0), ModelCheckpoint(kfold_weights_path, monitor='val_loss', …
python machine-learning neural-network conv-neural-network keras