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|BSD 3-clause "New" or "Revised" License||GNU General Public License v3.0 or later|
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Data Science toolset summary from 2021
13 projects | dev.to | 13 Nov 2021
Scikit-learn - It is one of the most widely used frameworks for Python based Data science tasks. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Link - https://scikit-learn.org/
Intel Extension for Scikit-Learn
4 projects | news.ycombinator.com | 1 Nov 2021
Currently some works is being done to improve computational primitives of scikit-learn to enhance its overhaul performances natively.
You can have a look at this exploratory PR: https://github.com/scikit-learn/scikit-learn/pull/20254
This other PR is a clear revamp of this previous one:
Scikit-Learn Version 1.0
11 projects | news.ycombinator.com | 14 Sep 2021
Just to clarify, scikit-learn 1.0 has not been released yet. The latest tag in the github repo is 1.0.rc2
Top 10 Python Libraries for Machine Learning
14 projects | dev.to | 9 Sep 2021
Website: https://scikit-learn.org/ Github Repository: https://github.com/scikit-learn/scikit-learn Developed By: SkLearn.org Primary Purpose: Predictive Data Analysis and Data Modeling
where is binary_metric function in sklearn package
1 project | reddit.com/r/learnmachinelearning | 20 Aug 2021
There is a function named binary_metric in https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/metrics/_base.py
Use Scikit-Learn and Runflow
2 projects | dev.to | 6 Jul 2021
If you're not familiar with Scikit-learn and Runflow,
Confused as to what exaclty a piece of code does
1 project | reddit.com/r/learnmachinelearning | 18 Jun 2021
well you can start at https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/model_selection/_validation.py, or maybe someone will guide you later
What Makes Python Libraries So Important For Data Science Learning?
3 projects | reddit.com/r/u_Snoo36930 | 16 Jun 2021
Next comes the complexity of drawing the maximum possible number of valuable insights. Using different python libraries such as Scikit-Learn, PyTorch, Pandas, etc., complications of data analysis can be solved within a minute. And the complexity associated with visualisation gets handled by other data visualisation libraries like Matploitlib, PyTorch, etc.
Is there a way to map cluster centers back to a dataframe?
1 project | reddit.com/r/learnpython | 19 May 2021
To avoid the issue with convergence (and the discrepancy between the labels_ and cluster_centers_), you can set tol=0, though this can of course lead to issues if convergence is a problem. There was an issue about it here. Assuming it's converged, then the order is fine.
Any from scratch Hamming Loss implementations?
1 project | reddit.com/r/LearnML | 10 May 2021
The source code for the function you refer to is quite straightforward anyway. The definition of count_nonzero() is here.
Dislike button would improve Spotify's recommendations
4 projects | news.ycombinator.com | 16 Oct 2021
I spent the latter half of 2019 trying to build this as a startup. Ultimately I pivoted (now I do newsletter recommendations instead), but if I hadn't made some mistakes I think it could've gotten more traction. Mostly I should've simplified the idea to make it easier to build. If anyone's interested in working on this, here's what I would do:
(But first some background: The way I saw it, you can split music recommendation into two tasks: (1) picking a song you already know that should be played right now, and (2) picking a new song you've never heard of before. (Music recommendation is unique in this way since in most other domains there isn't much value in re-recommending items). I think #1 is more important, and if you nail that, you can do a so-so job of #2 and still have a good system.)
Make a website that imports your Last.fm history. Organize the history into sessions (say, groups of listen events with a >= 30 minute gap in between). Feed those sessions into a collaborative filtering library like Surprise, as a CSV of `, , 1` (1 being a rating--in this case we only have positive ratings). Then make some UI that lets people create and export playlists. e.g. I pick a couple seed songs from my listening history, then the app uses Surprise to suggest more songs. Present a list of 10 songs at a time. Click a song to add it, and have a "skip all" button that gets a new list of songs. Save these interactions as ratings--e.g. if I skip a song, that's a -1 rating for this playlist. For some percentage of the suggestions (20% by default? Make it configurable), use Last.fm's or Spotify's API to pick a new song not in your history, based on the songs in the current playlist. Also sometimes include songs that were added to the playlist previously--if you skip them, they get removed from the playlist. Then you can spend a couple minutes every week refreshing your playlists. Export the playlists to Spotify/Apple Music/whatever.
As you get more users, you can do "regular" collaborative filtering (i.e. with different users) to recommend new songs instead of relying on external APIs. There are probably lots of other things you could do too--e.g. scrape wikipedia to figure out what artists have done collaborations or something. In general I think the right approach is to build a model for artist similarity rather than individual song similarity. At recommendation time, you pick an artist and then suggest their top songs (and sometimes pick an artist already in the user's history, and suggest songs they haven't heard yet--that's even easier).
This is the simplest thing I can think of that would solve my "I love music but I listen to the same old songs everyday because I'm busy and don't want to futz around with curating my music library" problem. You wouldn't have to waste time building a crappy custom music app, and users won't have to use said crappy custom music app (speaking from personal experience...). You wouldn't have to deal with music rights or integrating with Spotify/Apple Music since you're not actually playing any music.
If you want to go further with it, you could get traction first and then launch your own streaming service or something. (Reminds me a bit of Readwise starting with just highlights and then launching their own reader recently). I think it'd be neat to make an indie streaming service--kind of like Bandcamp but with an algorithm to help you find the good stuff. Let users upload and listen to their own MP3s so it can still work with popular music. Of course it'd be nicer for users in the short term if you just made deals with the big record labels, however this would help you not end up in Spotify's position of pivoting to podcasts so you can get out of paying record labels. And then maybe in a few decades all the good music won't be on the big labels anyway :).
Anyway if anyone is remotely interested in building something like this, I'll be your first user. I really need it. Otherwise I'll probably build it myself at some point in the next year or two as a side project.
Show HN: The Sample – newsletters curated for you with machine learning
1 project | news.ycombinator.com | 28 Jun 2021
I'm planning to build a business on this, so probably won't open-source it--but I'm always looking for interesting things to write about! I write a weekly newsletter called Future of Discovery; I might write up some more implementation details there in a week or two. In the mean time, most of the heavy lifting is done by the Surprise python lib. It's pretty easy to play around with, just give it a csv of , , and then you can start making rating predictions. Also fastText is easy to mess around with too. Most of the code I've written just layers things on top of that, e.g. to handle exploration-vs-exploitation as discussed in another thread here.
Recently I've been factoring out the ML code into a separate recommendation service so it can different kinds of apps (I just barely made this essay recommender system start using it for example).
I'm happy to chat about recommender systems also if you like, email's in my profile.
What are some alternatives?
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
Keras - Deep Learning for humans
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
tensorflow - An Open Source Machine Learning Framework for Everyone
gensim - Topic Modelling for Humans
python-recsys - A python library for implementing a recommender system
TFLearn - Deep learning library featuring a higher-level API for TensorFlow.
MLflow - Open source platform for the machine learning lifecycle
Crab - Crab is a ﬂexible, fast recommender engine for Python that integrates classic information ﬁltering recommendation algorithms in the world of scientiﬁc Python packages (numpy, scipy, matplotlib).
seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)