SummerOfCode2021
scikit-learn
SummerOfCode2021 | scikit-learn | |
---|---|---|
36 | 81 | |
12 | 58,130 | |
- | 0.5% | |
1.9 | 9.9 | |
about 2 years ago | 5 days ago | |
Python | ||
- | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
SummerOfCode2021
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Looking for junior developers to participate in open-source
https://summerofcode.withgoogle.com/ - sign up as an org, you have until the 21st.
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Any suggestions on joining GSoC Haskell.org?
I'm interested in Google summer of code event but missed it last year. I want to attend the event this year and notice that Haskell.org was one of the organizations. It sounds really exciting, I hope Haskell.org will participate in GSoC this year as well. But I have little experience with Haskell, I'm worried about how to write an expressive proposal when I don't even know which projects they will focus on. Can anyone give me so resources or suggestions about the following question?
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What Do I Need To Pass Resume Scan For FAANG Internships?
I also encourage you to look into Google Summer of Code (GSoC).
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Google calls for new government action to protect open-source software projects
OSS have benefited from Google Summer of Code, Google has released many fundamental libraries as OSS such as Guava for Java, Abseil for C++, etc. Many have criticized that Google never made money of a lot of its innovations, and instead had others build their own based on Google's shared information (Hadoop, and later all nosql databases, for a quick example; Docker came to be from open source innovations that Google put into Linux for their own container system). Even as Google has begun monetizing, they still open source core parts of their products (such as Kubernetes) for their benefit.
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If Google was smart....
They actually do something like that. It's called Summer of Code.
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Open Source Internships and Programs in 2022
Apply on their official page: Google Summer of Code
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How can I apply for GSoC 2022?
Read: https://summerofcode.withgoogle.com/
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Open Source Projects for Students in 2022
More at 👉 GSoC 2. The Linux Foundation Mentorship Program
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No internship. What should I do instead?
if you don't land anything, maybe consider Google Summer of Code.
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How I Ask Questions as a Software Engineer
I maintain a project called Meshery and one of the new contributors (who came in to get a GSoC internship) literally asked if I could explain what Meshery is.
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?
mcdowell-cv - A Nice-looking CV template made into LaTeX
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
cargo-auditable - Make production Rust binaries auditable
Surprise - A Python scikit for building and analyzing recommender systems
miragejs - A client-side server to build, test and share your JavaScript app
Keras - Deep Learning for humans
anki - Anki's shared backend and web components, and the Qt frontend
tensorflow - An Open Source Machine Learning Framework for Everyone
safety-dance - Auditing crates for unsafe code which can be safely replaced
gensim - Topic Modelling for Humans
Code-Server - VS Code in the browser
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.