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practicaldeeplearningpython's Introduction

Source code for "Practical Deep Learning: A Python-Based Introduction"

You'll find the source code included or referenced in the book in this archive. The code is organized by chapter. If the chapter is not listed, there was no code to go with it.

All the code is Python 3.X and requires the libraries installed in Chapter 1 of the book.

Please send questions, comments, or bugs to:

Updates:

page 84: the URL for the Iris dataset has changed:
            https://archive.ics.uci.edu/dataset/53/iris

page 86: the URL for the Breast Cancer dataset has changed:
            https://archive.ics.uci.edu/dataset/15/breast+cancer+wisconsin+original

TensorFlow issues addressable with:
            pip3 install tensorflow-cpu  (TF 2.8)
            then update repo code to move 'keras' imports to 'tensorflow.keras'

Moving from Adadelta to Adam:
            Adadelta appears to be broken in newer versions of TensorFlow.  Therefore, if you
            are getting poor performance, I suggest moving all models to Adam as the optimizer.
            Simply replace "Adadelta" with "Adam" and you should be good to go.

The file tutorial.pdf is a beginner's guide to NumPy, SciPy, Matplotlib, and Pillow.

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practicaldeeplearningpython's Issues

Code bug report.

On listing 4-1 code to construct the subsets using a 90/5/5 split of the original data (page 71).

❹ 
n0 = int(x0.shape[0]-ntrn0)
n1 = int(x1.shape[0]-ntrn1)
xval = np.zeros((int(n0/2+n1/2),20))
yval = np.zeros(int(n0/2+n1/2))
xval[:(n0//2)] = x0[ntrn0:(ntrn0+n0//2)]
xval[(n0//2):] = x1[ntrn1:(ntrn1+n1//2)]
yval[:(n0//2)] = y0[ntrn0:(ntrn0+n0//2)]
yval[(n0//2):] = y1[ntrn1:(ntrn1+n1//2)]

Lets assume for now that (for whatever reason) you end up with n0 = 5 and n1 = 5. If such was the case you wilt end up with something like this (replacing variables with actual values):

n0 = 5
n1 = 5
xval = np.zeros(5,20)
yval = np.zeros(5)
xval[:2] = x0[ntrn0:(ntrn0+2)]
xval[2:] = x1[ntrn1:(ntrn1+2)]  => Crash here.

The reason why the code will crash is because the last line in the sample code above is looking for a range of [2:], which really translates to [2:3]. When that is the case, (ntrn1+2) should be (ntrn1+3). Basically you are being inconsistent in the way you truncate the values throughout the procedure.

The end result is that you end up with a similar error as the one shown below.

image

Thanks.

URL for iris dataset changed (again)

The URL given as update in the README.md line 18 on the iris dataset would profit from changing to https://archive.ics.uci.edu/dataset/53/iris/:

diff old/README.md new/README.md
18c18
<                 https://archive.ics.uci.edu/ml/machine-learning-databases/iris/
---
>                 https://archive.ics.uci.edu/dataset/53/iris/

The one currently stated in the README.md yields a NOT FOUND.

Thanks for sharing through providing these resources (repositories), much appreciated.

All the best,
Stefan

All of the data directories are empty!

All of the subdirectories of data/ just have a README.txt file stating the data should go there, but it's not there.

I'm looking for the iris data to start with.

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