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..
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Lecture-01-introduction.ipynb
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Lecture-02-Variables-and-Functions.ipynb
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Lecture-03-Numpy.ipynb
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Lecture-04-Loops-vs-Vectorization.ipynb
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Lecture-05-Matplotlib.ipynb
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Lecture-06-Linear-Algebra.ipynb
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Lecture-07-Histogram.ipynb
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Lecture-08-Randomness.ipynb
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Lecture-09-Statistics.ipynb
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Lecture-10-Random-Walks.ipynb
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Lecture-11-Class.ipynb
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Lecture-12-Gradient-Descent.ipynb
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Lecture-13-Linear-Regression.ipynb
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Lecture-14-Linear-Regression-II.ipynb
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Lecture-15-Overfitting.ipynb
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Lecture-16-Classification-I.ipynb
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Lecture-17-Classification-II-Binary.ipynb
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Lecture-18-Classification-III-Multiclass.ipynb
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Lecture-19-Stochastic-Gradient-Descent.ipynb
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Lecture-20-K-Nearest-Neighbor.ipynb
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Lecture-21-Principal-Component-Analysis.ipynb
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Lecture-22-Neural-Network-I.ipynb
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Lecture-23-Neural-Network-II.ipynb
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Lecture-24-Advanced-Tricks.ipynb
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math10.py
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neural_net.png
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neural_net_3l.png
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neuron-1.png
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weights.npz
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