Based on our record, Scikit-learn should be more popular than hub. It has been mentiond 29 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Use hub here via CLI and forget the gui https://hub.github.com/. - Source: Hacker News / 10 months ago
Try automating the PR process as much as possible. Make use of tools like hub CLI for speeding up the pull request process. Code quality tools can help you automate the due diligence for coding standards and conventions, and test automation tools can assist in bug discovery, and identifying security vulnerabilities. - Source: dev.to / about 1 year ago
Parse_git_branch() { # Speed up opening up a new terminal tab by not # checking `$HOME` ...which can't be a repo anyway # # For the heck of it, micro-optimize this too: # time (repeat 1000000 { [ "$PWD" = "$HOME" ] } ) == ~4.2s # time (repeat 1000000 { [[ "$PWD" == "$HOME" ]] } ) == ~1.4s [[ "$PWD" == "$HOME" ]] && return # Fastest known way to check the current branch name ... Source: almost 2 years ago
You can always query via github api or use the hub client (from their home page https://hub.github.com/). Source: over 2 years ago
How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 20 days 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 / 4 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 / about 1 year 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: about 1 year 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: about 1 year ago
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