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| Dear Giulio, Thank you for your valuable information -- I really appreciate it. I have one more question. In my current setup, the response variable is a numerical value rather than a categorical one. I've created a simple training dataset and... |
| I removed the constants to make the ODE simple and used constants for initial conditions, which can be modified, accordingly. (* 1st approach *) dsolSDL03 = DSolve[ {D[y[x], {x, 4}] + D[y[x], {x, 2}] == 0, y[0] == 4,... |
| I am not sure about the conditions: why "0" and x->"0" with quotation? su["0", y] == 1/Cosh[y], D[su[x, y], x] == 0 /. x -> "0". I replaced the quotations and the PDE can be solved su[0, y] == 1/Cosh[y], D[su[x, y], x] == 0 ... |
| Thanks for this great notebook. |
| The "matrice" in your example is ill - conditioned; thus, the computation of its inverse matrix is unstable (e.g., a small change in a matrix causes huge changes in the inverse matrix). I believe you already know the followings but just a reminder. ... |
| NumericalArray[] returns an object, not a matrix. Use Normal[], e.g., f2=Fourier[Normal[dataNum]] |
| I just find out the "Exploratory Factor Analysis" from the Wolfram Demonstrations Project, that provides the varimax rotation. [http://demonstrations.wolfram.com/ExploratoryFactorAnalysis/][1] [1]:... |
| Hi, I am reading your another book titled "Neural network and deep learning with Mathematica" and found it very useful, especially the chapters 4 and 5. Mathematica has good documentation for individual layers but I found it is difficult to put... |
| HI, This is a good example about how to run mathematica from Python. I think it is better to change the title of this posting for better search, e.g., how to run Mathematica from Python |
| Per your interest on predicting probabilities, you can compute the probabilities for classification, by using the "net3" and the "trainedNet3" which use the SoftmaxLayer and the output as a string (e.g., "1", not 1.0. {finalNet3[{9987, 13},... |