Based on our record, Carbon should be more popular than Scikit-learn. It has been mentiond 167 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.
Carbon is a free online code screenshot tool that helps users create beautiful code screenshots for use in blogs, social media, or presentations. It provides a simple interface that allows users to enter their own code and choose different themes, fonts, and color schemes. Users can also adjust the code alignment, line numbers, background, shadow, etc. To better control the screenshot effect. Carbon also supports... - Source: dev.to / 2 months ago
Carbon, an online code beautification tool, lets you create visually appealing code screenshots with a simple interface, enhancing code readability. - Source: dev.to / 4 months ago
Carbon.now.sh - create and share code snippets in an aesthetic screenshot-like image format. Usually used to aesthetically share/show off code snippets on Twitter or blog posts. - Source: dev.to / 4 months ago
You could try Carbon: https://carbon.now.sh/. - Source: Hacker News / 8 months ago
Carbon allows you to create stunning and customizable code screenshots with syntax highlighting. Whether you want to share code snippets on social media or enhance your documentation, Carbon is a handy tool to have in your arsenal. - Source: dev.to / 8 months 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 / 5 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 / 3 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
Ray.so - Create beautiful images of your code
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Snappify - snappify is a great tool to create and adjust beautiful code snippets easily.
OpenCV - OpenCV is the world's biggest computer vision library
Codeimg.io - Create and share images of your source code
NumPy - NumPy is the fundamental package for scientific computing with Python