# Tag Info

### Did Minsky and Papert know that multi-layer perceptrons could solve XOR?

There does not appear to be a historical consensus on this. The Wikipedia page on the Perceptrons book (which does not come down on either side) gives an argument that the ability of MLPs to compute ...
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### Why is it called back-propagation?

Why is it called back-propagation? I don't think there is anything special here! It's called back-propagation (BP) because, after the forward pass, you compute the partial derivative of the loss ...
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### Why use a recurrent neural network over a feedforward neural network for sequence prediction?

An RNN or LSTM have the advantage of "remembering" the past inputs, to improve performance over prediction of a time-series data. If you use a neural network over like the past 500 characters, this ...
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### Did Minsky and Papert know that multi-layer perceptrons could solve XOR?

Whether Minsky knew or not, it was definitely known to Rosenblatt, as he published those results in his really pioneering report - Principles of Neurodynamics: Perceptrons and the Theory of Brain ...
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Accepted

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### Why is a simple regression problem so hard for an MLP to learn?

An interesting problem. This network has only 933 trainable parameters, and obtains MeanAbsolutePercentageError of 0.01 - 0.04. It is based on a softmax activation, ...
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### What are some datasets to train an MLP on simple tasks?

A popular dataset is the fisher iris dataset. It consists of 150 samples each with a dimensionality of 4. You can find it at http://archive.ics.uci.edu/ml/datasets/Iris
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### Using a MLP to predict a 12x12 matrix

The short answer to your question is: you probably do not fully know your data. remember that ML is no magic wand. It needs your understanding of the data and the behavior of it. Although it is ...
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### Comments on my proposed "Jitter" neuron

Well, adding gaussian noise is a very common regularisation method. Maybe this paper is interesting to you. They also have very small datasets. In the end there is only so much you can get out of a ...
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### How can a neural network learn to play sudoku?

You can take a look at this paper that solving your problem with a neural network. You can use the pytorch implementation of the satnet layer : satnet layer API. In this supervised setup the layer ...
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Accepted

### Is it expected that adding an additional hidden layer to my 3-layer ANN reduces accuracy significantly?

You probably got the back propagation wrong. I have done a test on the accuracy on adding an extra layer and the accuracy went up from 94% to 96% for me. See this for details: https://colab.research....
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Accepted

### Are the labels updated during training in the algorithm presented in "An algorithm for correcting mislabeled data"?

I think that making some draws might help. Below I tried to draw the model architecture. We start with classic feed-forward structure: input represented by a vector I with length f (number of ...
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Accepted

### Why is it called back-propagation?

Have a look at the following article Principles of training multi-layer neural network using backpropagation. It was very useful to me. You can also see here an example of backpropagation in Matlab. ...
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Accepted

### Why does every neuron in hidden layers of a multi-layer perceptron typically have the same activation function?

As you stated, it's popular to have some form of a rectified linear unit (ReLU) activation in hidden layers and the output layer is often a softmax or sigmoid (depending also on the problem: multi-...
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1 vote

### Recent algorithms for correcting mislabeled data using multilayer perceptrons

The most general solution today for the problem of finding label errors in datasets is called "confident learning" which works for all datasets and models, can be run time-efficiently in one ...
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1 vote
Accepted

### Why does my model overfit on pseudo-random numbers training data?

Simply said, predicting pseudo random number is just not possible for now. Pseudo random numbers generated now have a high enough "randomness" so that it cannot be predicted. Pseudo random numbers is ...
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