query: Neural network from scratch: only predicts training inputs correctly
positive_passages.docid: 10993
positive_passages.text: What should I do when my neural network doesn't generalize well?
negative_passages.docid: 10855
negative_passages.text: How to use early stopping properly for training deep neural network?
negative_passages.docid: 48226
negative_passages.text: How to adjust the hyperparameters of MLP classifier to get more perfect performance
negative_passages.docid: 15759
negative_passages.text: Adding Regressors to Convolutional Neural Network
negative_passages.docid: 110479
negative_passages.text: Why doesn't backpropagation work when you initialize the weights the same value?
negative_passages.docid: 126373
negative_passages.text: Can we use MLE to estimate Neural Network weights?
negative_passages.docid: 13412
negative_passages.text: Motivating sigmoid output units in neural networks starting with unnormalized log probabilities linear in $z=w^Th+b$ and $\phi(z)$
negative_passages.docid: 66215
negative_passages.text: Training a decision tree against unbalanced data
negative_passages.docid: 63049
negative_passages.text: Train loss and accuracy
negative_passages.docid: 58845
negative_passages.text: Neural networks output probability estimates?
negative_passages.docid: 44176
negative_passages.text: Deep Learning for Regression
negative_passages.docid: 84469
negative_passages.text: Is there a thumb-rule for designing neural-networks?
negative_passages.docid: 122549
negative_passages.text: decision boundary of perceptron too small
negative_passages.docid: 92956
negative_passages.text: Is it possible to train a neural network without backpropagation?
negative_passages.docid: 70893
negative_passages.text: How to Identify Overfitting in Convolutional Neural network?
negative_passages.docid: 94732
negative_passages.text: Why do neural networks need so many training examples to perform?
negative_passages.docid: 74904
negative_passages.text: Random initialization/order in neural network — bias or variance?