Code-used-on-Daniel-Lemire-s-blog
scikit-learn
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Code-used-on-Daniel-Lemire-s-blog | scikit-learn | |
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24 | 81 | |
791 | 58,130 | |
- | 1.1% | |
9.4 | 9.9 | |
5 days ago | 2 days ago | |
C | Python | |
- | BSD 3-clause "New" or "Revised" License |
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Code-used-on-Daniel-Lemire-s-blog
- Estimating Your Memory Bandwidth
- First 96-Core AMD Zen 4 Threadripper Tests Show Utter Domination over Intel
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Parsing time stamps faster with SIMD instructions
It's not bad at all https://github.com/lemire/Code-used-on-Daniel-Lemire-s-blog/...
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Under Linux, libSegFault and addr2line are underrated
A newline is missing in the example code. As given there's a segfault at line 5 not line 6.
However, the code at https://github.com/lemire/Code-used-on-Daniel-Lemire-s-blog/... shows it's indeed at line 6, because it has an extra newline after the '#include '.
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Best Websites For Coders
Daniel Lemire's Blog : Daniel Lemire's blog
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Technical Blogs You Recommend?
Dr. Daniel Lemire's blog: https://lemire.me/blog, covers lots of technical items on optimizations in various programming languages, Lemire's work is currently in use across a number of projects and he consistently delivers fantastic improvements, he usually accompanies these improvements with a blog post describing what he did. He also occasionally posts interesting Science and Technology links on various topics not limited to tech, but health and education as well.
- suggest some c language blogs....
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What are some cool modern libraries you enjoy using?
Nope, simdjson is originally from Daniel Lemire who also often blogs about fancy low level optimizations: https://lemire.me/blog/ I'm just a happy user :)
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Escaping strings faster with AVX-512
Added this pull request with some interesting results.
There's a copy of the loop used on the escape function inside the avx512_escape function [0]. Is it needed or just a copy and paste mistake? (I know nothing about vector instructions)
0: https://github.com/lemire/Code-used-on-Daniel-Lemire-s-blog/...
scikit-learn
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AutoCodeRover resolves 22% of real-world GitHub in SWE-bench lite
Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:
- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...
- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.
There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).
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Polars
sklearn is adding support through the dataframe interchange protocol (https://github.com/scikit-learn/scikit-learn/issues/25896). scipy, as far as I know, doesn't explicitly support dataframes (it just happens to work when you wrap a Series in `np.array` or `np.asarray`). I don't know about PyTorch but in general you can convert to numpy.
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[D] Major bug in Scikit-Learn's implementation of F-1 score
Wow, from the upvotes on this comment, it really seems like a lot of people think that this is the correct behavior! I have to say I disagree, but if that's what you think, don't just sit there upvoting comments on Reddit; instead go to this PR and tell the Scikit-Learn maintainers not to "fix" this "bug", which they are currently planning to do!
- Contraction Clustering (RASTER): A fast clustering algorithm
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Ask HN: Learning new coding patterns – how to start?
I was in a similar boat to yours - Worked in data science and since then have made a move to data engineering and software engineering for ML services.
I would recommend you look into the Design Patterns book by the Gang of Four. I found it particularly helpful to make extensible code that doesn't break specially with abstract classes, builders and factories. I would also recommend looking into the book The Object Oriented Thought Process to understand why traditional OOP is build the way it is.
You can also look into the source code of popular data science libraries such as sklearn (https://github.com/scikit-learn/scikit-learn/tree/main/sklea...) and see how a lot of them have Base classes to define shared functionality between object of the same nature.
As others mentioned, I would also encourage you to try and implement design patterns in your everyday work - maybe you can make a Factory to load models or preprocessors that follow the same Abstract class?
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Transformers as Support Vector Machines
It looks like you've been the victim of some misinformation. As Dr_Birdbrain said, an SVM is a convex problem with unique global optimum. sklearn.SVC relies on libsvm which initializes the weights to 0 [0]. The random state is only used to shuffle the data to make probability estimates with Platt scaling [1]. Of the random_state parameter, the sklearn documentation for SVC [2] says
Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.
[0] https://github.com/scikit-learn/scikit-learn/blob/2a2772a87b...
[1] https://en.wikipedia.org/wiki/Platt_scaling
[2] https://scikit-learn.org/stable/modules/generated/sklearn.sv...
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How to Build and Deploy a Machine Learning model using Docker
Scikit-learn Documentation
- Planning to get a laptop for ML/DL, is this good enough at the price point or are there better options at/below this price point?
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Link Prediction With node2vec in Physics Collaboration Network
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy.
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WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole.
What are some alternatives?
FastPFor - The FastPFOR C++ library: Fast integer compression
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
farmhash - Automatically exported from code.google.com/p/farmhash
Surprise - A Python scikit for building and analyzing recommender systems
rust - Empowering everyone to build reliable and efficient software.
Keras - Deep Learning for humans
simonwillisonblog - The source code behind my blog
tensorflow - An Open Source Machine Learning Framework for Everyone
developer-roadmap - Interactive roadmaps, guides and other educational content to help developers grow in their careers.
gensim - Topic Modelling for Humans
cheatsheets - Cheatsheets for web development - devhints.io
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.