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Generating a website for your R package is always a great idea. If the package is based on some paper, it will help it get noticed and eventually used. And once you have a website, it's just as well to include a reference manual for the package in it, that complements or is a bit more updated than the one published in CRAN. Or simply in another format. - Source: dev.to / 26 days ago
This package is definitely related to R language) (see package URL, it points to r-project.org subdomain). Source: over 1 year ago
Common misconception. Actually it's a Fibonacci sequence, so the next one is https://rrrrr-project.org. This does also mean that there's https://-project.org, and that https://r-project.org secretly disambiguates into two different projects. - Source: Hacker News / almost 2 years ago
We already have https://r-project.org. Now we have https://rr-project.org. So, https://rrr-project.org is next? - Source: Hacker News / almost 2 years ago
Thank you, but unfortunately, the archive I'm talking about is the archive of old package versions, which seems to only be available through r-project.org. Source: almost 2 years ago
Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / 2 months ago
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. - Source: dev.to / 11 months ago
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. Source: 12 months ago
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: 12 months ago
Scikit-learn is a machine learning library that comes with a number of pre-built machine learning models, which can then be used as python wrappers. Source: about 1 year ago
C++ - Has imperative, object-oriented and generic programming features, while also providing the facilities for low level memory manipulation
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Perl - Highly capable, feature-rich programming language with over 26 years of development
OpenCV - OpenCV is the world's biggest computer vision library
D (Programming Language) - D is a language with C-like syntax and static typing.
NumPy - NumPy is the fundamental package for scientific computing with Python