scientific-visualization-book
scikit-learn
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scientific-visualization-book | scikit-learn | |
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17 | 81 | |
10,057 | 58,046 | |
- | 1.0% | |
3.6 | 9.9 | |
3 months ago | 6 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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scientific-visualization-book
- Scientific Visualization: Python and Matplotlib
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Which latest DS Skill you are working on currently?
knowing matplotlib really well gets really pro viz tbh, this https://github.com/rougier/scientific-visualization-book is the best resource for it imo. Its a bit more work but you can get really great results
- Book or web book recommendation request: a data visualization cookbook using Python for scientists.
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What's New in Matplotlib 3.6.0
I had the same problem until I found this tutorial:
https://github.com/rougier/matplotlib-tutorial
If you wan something deeper the same person has written a book:
https://github.com/rougier/scientific-visualization-book
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looking for scientific visualization book in julia
i saw this one : > https://github.com/rougier/scientific-visualization-book
- Scientific-Visualization-Book - None
- 📘 An open access book on scientific visualization using python and matplotlib, h/t @MikeTamir
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Dyson hatching (dungeon map)
I re-created the hatching using matplotlib as shown here.
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Dungeon map rendering using matplotlib
From the open access book "Scientific Visualization: Python + Matplotlib. Code: dungeon.py
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Ask HN: What is the best book on data visualization in 2021?
For python this open access book is excellent: https://github.com/rougier/scientific-visualization-book
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?
datatable - A Python package for manipulating 2-dimensional tabular data structures
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
sktime - A unified framework for machine learning with time series
Surprise - A Python scikit for building and analyzing recommender systems
db-benchmark - reproducible benchmark of database-like ops
Keras - Deep Learning for humans
DataFrame - C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
tensorflow - An Open Source Machine Learning Framework for Everyone
Altair - Declarative statistical visualization library for Python
gensim - Topic Modelling for Humans
oz - Data visualizations in Clojure and ClojureScript using Vega and Vega-lite
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.