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Data science, Machine learning, and Artificial Intelligence. We post daily contents related to machine learning focusing on Numpy, Pandas, and ML effectively.
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Hello Data Science & NumPy enthusiasts πŸ‘‹πŸ‘‹.Of course it’s time for yet another episode on NumPy πŸ™.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, it’s needed to stack them to be able to feed them to the next layer of the network.So it’s supeeeer helpful to know how to do that in NumPy and voila it’s not so bad with np.vstack and np.hstack, you can stack up your arrays as long as the sizes match in the direction you’re stacking πŸ‘.Happy stacking πŸ˜‚πŸ‘Œ
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πŸ‘¨β€πŸ’»#NumPy
‼️ To ensure every new post is visible to you, please turn on post notification at the top right of the post ‼️.Welcome to part 7 of our journey through NumPy πŸ‘‹.When it comes to data manipulation, being able to filter data points of various ranges is a must πŸ‘.NumPy makes it really easy to filter data points and reset their values πŸ‘Œ.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 πŸ™.How are you planning to use NumPy in your projects ⁉️❓
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πŸ‘¨β€πŸ’»#NumPy
Welcome back to another NumPy lesson! πŸ‘‹.Basketball players make a great excuse to learn about arg functions in NumPy πŸ€.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 πŸ‘Œ.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 🀯😏.
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πŸ‘¨β€πŸ’»#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) πŸ‘Œ
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πŸ‘¨β€πŸ’»#NumPy
Part 10 πŸŽ‰ 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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πŸ‘¨β€πŸ’»#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!! πŸŽ‰πŸ‘Œ
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Typo alert: 4th slide should say np.array([[6], [7]]) for the picture to match!
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np.cumsum() is a useful function when it comes to doing big data cumulative sums. See it, learn it, and use it πŸ’ͺ
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πŸ‘¨β€πŸ’»#NumPy