LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives.
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Music-Map
Radiooooo
Gnoosic
Poly-graph Hip Hop
Every Noice at Once
Tempso
This Is My Jam
Because Recollection
Scikit-learn
Pandas
OpenCV
NumPy
Dataiku
WEKA
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Music-Map Scikit-learnNo Music-Map videos yet. You could help us improve this page by suggesting one.
Scikit-learn might be a bit more popular than Music-Map. We know about 29 links to it since March 2021 and only 20 links to Music-Map. 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.
I can't answer your question, but music-map has helped me find similar stuff to my favourite artists before. https://music-map.com. - Source: Hacker News / 5 months ago
My suggestion is you head over to music-map.com and type in the names of some artists you enjoy. The algorithm will then put up a cloud of bands/artist recommendations. Source: over 1 year ago
Have you ever fucked around on everynoise.com and music-map.com? Have fun! Source: over 1 year ago
The artists were picked either from me listening and enjoying 1 of their albums, or using the site music-map.com and finding similar artists that I already do enjoy. Source: over 1 year ago
Go to music-map and put an artist's name in the search box to find similar artists. Source: over 1 year 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 / 18 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
Radiooooo - Web radiooooo offering users a brand new and amazing musical experience: select a country on a...
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Gnoosic - Even if you don't know what you are looking for - gnod will find it.
OpenCV - OpenCV is the world's biggest computer vision library
Poly-graph Hip Hop - See what hip hop's billboard top 10 sounded like
NumPy - NumPy is the fundamental package for scientific computing with Python