The tag has no usage guidance.

learn more… | top users | synonyms

4
votes
0answers
278 views

Is there any decent deep learning library in Mathematica? [closed]

Deep Learning is the currently trending method in machine learning community. Some languages like python and matlab have some very convenient toolbox for deep learning. So is there any decent deep ...
3
votes
0answers
62 views

Setting MaxIterations for neural network function `Predict`?

The neural network implementation of Predict seems to support a number of undocumented options as shown in a previous question: How to change NeuralNetwork ...
4
votes
0answers
116 views

Neural Networks in Predict function

I'm trying to reproduce the result giving by a Predict function with a Neural Network as the chosen method. So, I'm training from this set: ...
3
votes
0answers
66 views

Undo automatic update

I was working with Predict function (with a neural network and today, and when I executed the same notebook I have been working for some time, Machine Learning package was automatically updated (it ...
1
vote
1answer
176 views

Creating an animated graph model based on equations

So I have a graph that looks like this ...
0
votes
1answer
77 views

What's going wrong with this button! [duplicate]

I'm implementing an Adaline algorithm where all inputs are 1x5526 and the output is a number from 1 to 10. I have this two functions defined and worked outside the ...
0
votes
1answer
329 views

Mean path length of randomly generated graphs

I am looking to find the the average path length of 1000 random graphs with the following degree distribution, a few of the vertex degrees are included below ...
3
votes
1answer
120 views

Debugging the context of a Package

I am working on a package of functions that complement the NeuralNetworks` package that Wolfram provides. I am having a problem with the following code, which is ...
2
votes
0answers
243 views

How to predict some constant number of values with NeuralNetworks

Example: << NeuralNetworks` data = {Sin[#]} & /@ Range[0, 4 Pi, 0.5]; learn = data[[1 ;; 15]]; Now, lets fit our neural network: ...