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ketanvijayvargiya.com | ||
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www.jamesridgway.co.uk
Querying large datasets can often be challenging, especially when performance is a key concern. Achieving performance at scale often comes with an element of trade-off in how a system is designed to achieve the desired functionality and performance at scale. A bloom filter is one example of a probabilistic data |
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connorboyle.io
TL;DR: if you are using scikit-learn 1.3.X and use f1_score() or classification_report() with the argument zero_division=1.0 or zero_division=np.nan1, then there's a chance that the output of that function is wrong (possibly by any amount up to 100%, depending on the number of classes in your dataset). E.g. for zero_division=1.0: In this post, np.nan refers to numpy.nan? |
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backdrifting.net
[AI summary] The author explains what Bloom Filters are, their limitations, various applications like pre-caching, and the mathematical theory behind using them as constant-space probabilistic data structures. |
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jaketae.github.io
Recently, a friend recommended me a book, Deep Learning with Python by Francois Chollet. As an eager learner just starting to fiddle with the Keras API, I decided it was a good starting point. I have just finished the first section of Part 2 on Convolutional Neural Networks and image processing. My impression so far is that the book is more focused on code than math. The apparent advantage of this approach is that it shows readers how to build neural networks very transparently. It's also a good introduction to many neural network models, such as CNNs or LSTMs. On the flip side, it might leave some readers wondering why these models work, concretely and mathematically. This point notwithstanding, I've been enjoying the book very much so far, and this post is... |
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