query: Is it possible to define what the last hidden layer represents in a neural net?
positive_passages.docid: 25120
positive_passages.text: Can't deep learning models now be said to be interpretable? Are nodes features?
negative_passages.docid: 6995
negative_passages.text: Neural Network: What if there are multiple right answers for a given set of inputs?
negative_passages.docid: 129280
negative_passages.text: Neural networks: why do we randomize the training set?
negative_passages.docid: 106705
negative_passages.text: Interpreting the end of Inception
negative_passages.docid: 126373
negative_passages.text: Can we use MLE to estimate Neural Network weights?
negative_passages.docid: 17817
negative_passages.text: Diagram of an artificial neural network
negative_passages.docid: 91230
negative_passages.text: Danger of setting all initial weights to zero in Backpropagation
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: 145813
negative_passages.text: Rigorous Logic of LSTM
negative_passages.docid: 80741
negative_passages.text: Constrain a Neural Network to be monotonic?
negative_passages.docid: 41356
negative_passages.text: Reason for square images in deep learning
negative_passages.docid: 58545
negative_passages.text: What does an Input layer of shape=(None,) or (None,12) actually mean?
negative_passages.docid: 31073
negative_passages.text: "Hidden layer sizes" vs "Hidden layers sizes"
negative_passages.docid: 49535
negative_passages.text: Deep learning : How do I know which variables are important?
negative_passages.docid: 106029
negative_passages.text: How to prepare/augment images for neural network?
negative_passages.docid: 79882
negative_passages.text: Explanation of Spikes in training loss vs. iterations with Adam Optimizer
negative_passages.docid: 109966
negative_passages.text: How does the Adam method of stochastic gradient descent work?