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This is a great repository - thanks!

I look forward to looking at your solutions. But what I really want first is a version of this repo that has the underlying plumbing for each exercise, without the solutions, so people can work out solutions on their own. I'm initially interested in Andrew Ng's "Machine Learning" class, exercises 3 and 4 on Neural Nets.

Is that already here somewhere? Or can you strip out your solutions and give us essentially a Python equivalent to the original assignments in Octave?

Originally posted by @helshafaie46 in #14

I was super excited to the find this repository. I've been wanting to draw graphs using Bokeh. I copied over the NetworkX notebook and replaced the matplotlib plotting with Bokeh. See the notebook example.

Thank you

Love these machine learning exercises! Just completed exercise 3 but came across one issue in the regularized cost function. I'm getting a divide by 0 error in the second var. I assumed this is because log(0) is undefined. Any way around this?

When i compile w/ Python 2.7 the accuracy is around 97% but only 76% when compiled with Python 3.

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I have this error when running the notebook with "jupyter notebook"

UnicodeDecodeError                        Traceback (most recent call last)
<ipython-input-8-42c04a259fe9> in <module>()
      1 file = open(train_datasets[0])
----> 2 train_A = pickle.load(file)
      3 train_A.shape

/usr/lib/python3.5/codecs.py in decode(self, input, final)
    319         # decode input (taking the buffer into account)
    320         data = self.buffer + input
--> 321         (result, consumed) = self._buffer_decode(data, self.errors, final)
    322         # keep undecoded input until the next call
    323         self.buffer = data[consumed:]

UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte

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def gradientDescent(X, y, theta, alpha, iters):
temp = np.matrix(np.zeros(theta.shape))
parameters = int(theta.ravel().shape[1])
cost = np.zeros(iters)

//why are we using below loop? Its a matrix multiplication I dont think we need to
//loop here. It will always give the same answer. Can you please tell me what is the benefit of using loop here?
for i in range(iters):
error = (X * theta.T) - y

About the mapped feature part.
The polynomial permuted features data set should have 28 features but you have way less in the notebook.

Is it your intention to not fully polynomialize features? or it's a bug in your permutation code?

Thanks for the notebook.

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