python_koans
scikit-learn
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python_koans | scikit-learn | |
---|---|---|
14 | 81 | |
4,864 | 58,130 | |
- | 1.1% | |
1.0 | 9.9 | |
about 2 months ago | 1 day ago | |
Python | Python | |
MIT License | 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.
python_koans
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Gaming VS Programming
- python
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How difficult is it to get into the IT field?
The learn to code in 2 months boot camps are largely rubbish but self teaching the basics in something like Python (I like the Koans https://github.com/gregmalcolm/python_koans as they will reach you coding, Google skills, testing, and source control) will at least tell you if you like coding.
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A question about efficiency : Coin flip from Automate The Boring Stuff With Python.
This reminds me of the Python Koans .. I loved doing those :) https://github.com/gregmalcolm/python_koans
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Learning Python3 after already knowing C++
After that, I'd work through something interactive. Lots off choices here, but maybe something like the Python koans: https://github.com/gregmalcolm/python_koans
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Seeking Advice on Mentoring a New Python Developer
When I started my current job I was given a couple of weeks to learn python. (I was a ruby dev previously). I really enjoyed the Python koans (https://github.com/gregmalcolm/python_koans) as a change from just reading docs, and something that gave me some small sense of achievement
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Struggle with python.
I'm learning a new language right now and I'm finding its "koans" helpful. "Koans" are a format for tutorials that take the form of a script that runs on your computer, that you gradually fix up like Stardew Valley. There are Python koans here - https://github.com/gregmalcolm/python_koans
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Good FREE Python learning websites?
Not exactly what you asked for but a good addition to learning. https://github.com/gregmalcolm/python_koans
- Anyone know any cheap/free resources to learn python over the summer?
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Learning Python
github.com/gregmalcolm - Python Koans
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Old guy programmer here, need to brush up on Python quickly!
When I was learning Ruby for a job ruby koans was helpful. Python has a version here: https://github.com/gregmalcolm/python_koans not sure of the quality though.
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?
Tiny-Python-3.6-Notebook - This repository contains the text for the Tiny Python 3.6 Notebook.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Python Cheatsheet - All-inclusive Python cheatsheet
Surprise - A Python scikit for building and analyzing recommender systems
Think-Python-2E-My_solutions - My solutions to the exercises contained in the "Think Python 2nd Edition" book by Allen B. Downey.
Keras - Deep Learning for humans
javascript-koans - Koans to learn Javascript
tensorflow - An Open Source Machine Learning Framework for Everyone
materials - Bonus materials, exercises, and example projects for our Python tutorials
gensim - Topic Modelling for Humans
adventofcode - Advent of Code solutions of 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 and 2023 in Scala
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.