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License: MIT License
Plain python implementations of basic machine learning algorithms
License: MIT License
I think you should also mention about data preprocessing. It is one of the important and basic step of machine learning.
Would be nice to see a naive bayes implementation.
very good.
in chapter "linear-regression":
"n_iters" is omitted in section "training with gradient descent";
"c='orange'" is omitted in section "visualize test predictions";
in chapter "logistic-regression":
"* 100" is omitted in section "testing the model";
in chapter "k-nearest-neighbor":
"regregression" may be typo at the start;
"plt.show()" is omitted in section "dataset";
"test accuracy with k = 8" and "*8-like" characters may be typos, must be 4;
in chapter "simple neural net":
"\textit{sigmoid}" may be typo in section "forward pass";
in chapter "softmax regression":
"one-hot encoded" in section "step iii", confused by "So
continuous updating...
Hi,
I am new to machine learning. I already know advanced python. Currently, I am using anaconda spyder for my codes, but I was hoping somebody could recommend an editor solely for machine learning and focused on "pandas" as well as "NumPy".
Hi, what is the license of this work? Thanks a lot for sharing :)
(if you're not familiar with choosing licenses, Github has put up a website to pick one in addition to their page about licensing a repo.)
Can I work on Random Forest Classifier issue?
I had a tough time understanding LSTM completely and I believe there are many out there who may have doubts on it. It would be awesome to study LSTM (or any recurrent NN) with plain Python.
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