Hello Data Science & NumPy enthusiasts ππ.Of course itβs time for yet another episode on NumPy π.Stacking arrays horizontally and vertically is something I do almost everyday.When you train large networks, your data becomes very large arrays of features, and very often, itβs needed to stack them to be able to feed them to the next layer of the network.So itβs supeeeer helpful to know how to do that in NumPy and voila itβs not so bad with np.vstack and np.hstack, you can stack up your arrays as long as the sizes match in the direction youβre stacking π.Happy stacking ππ
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π¨βπ»#NumPy
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π¨βπ»#NumPy
βΌοΈ To ensure every new post is visible to you, please turn on post notification at the top right of the post βΌοΈ.Welcome to part 7 of our journey through NumPy π.When it comes to data manipulation, being able to filter data points of various ranges is a must π.NumPy makes it really easy to filter data points and reset their values π.I use this technique daily in my job to get the data in the form I need before feeding it to my machine learning model π.How are you planning to use NumPy in your projects βοΈβ
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π¨βπ»#NumPy
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π¨βπ»#NumPy
Welcome back to another NumPy lesson! π.Basketball players make a great excuse to learn about arg functions in NumPy π.argmax() returns the index of the element with the maximum value.argmin() returns the index of the element with the minimum value.argsort() returns the indices of the array in ascending order π.Basically what adding arg to the name of the function does is to make return the *index* of the element and not the element itself.This is handy for this example because we then use the index to retrieve the name of the player from the other array π€―π.
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π¨βπ»#NumPy
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π¨βπ»#NumPy
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NumPy part 9: np.where()
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np.where() returns the indices where the condition is met (not the elements themselves) π
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π¨βπ»#NumPy
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NumPy part 9: np.where()
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np.where() returns the indices where the condition is met (not the elements themselves) π
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π¨βπ»#NumPy
Part 10 π of Intro to NumPy, what a journey guys, thanks for sticking around for these posts!! On to part 100 shall we??
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Follow u0040bigdataguru for tutorials and instructional posts on AI, machine learning and deep learning!
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π¨βπ»#NumPy
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Follow u0040bigdataguru for tutorials and instructional posts on AI, machine learning and deep learning!
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π¨βπ»#NumPy
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np.tile() is one of the most beautiful yet super useful functions there is in NumPy and Python! Happy weekend!! ππ
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Typo alert: 4th slide should say np.array([[6], [7]]) for the picture to match!
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π¨βπ»#NumPy
np.tile() is one of the most beautiful yet super useful functions there is in NumPy and Python! Happy weekend!! ππ
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Typo alert: 4th slide should say np.array([[6], [7]]) for the picture to match!
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π¨βπ»#NumPy
np.cumsum() is a useful function when it comes to doing big data cumulative sums. See it, learn it, and use it πͺ
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π¨βπ»#NumPy
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π¨βπ»#NumPy