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Domain: towardsdatascience.com Added: 2025-07-12 Status: βœ“ Success
data-science,machine-learning,tutorial,towardsdatascience.com
Recommendation System Perhaps the most famous recommender system is the so-called matrix factorization. In this collaborative recommender, users and items are represented with an embedding, which is n...
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https://towardsdatascience.com/introduction-to-embedding-based-recommender-systems-956faceb1919/
Recommendation System They are everywhere: these sometimes fantastic, sometimes poor, and sometimes even funny recommendations on major websites like ...
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https://towardsdatascience.com/recommender-systems-a-complete-guide-to-machine-learning-models-96d3f94ea748/
Recommender Systems: Why And How? Recommender systems are algorithms providing personalized suggestions for items that are most relevant to each user....
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Max Lin on finishing second in the R Challenge
https://medium.com/kaggle-blog/max-lin-on-finishing-second-in-the-r-challenge-520a7d785beb?source=rss----4b0982ce16a3---4
Max Lin on finishing second in the R Challenge I participated in the R package recommendation engine competition on Kaggle for two reasons. First, I u...
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https://github.com/benfred/implicit
Fast Python Collaborative Filtering for Implicit Datasets. This project provides fast Python implementations of several different popular recommendati...
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https://towardsdatascience.com/how-to-build-popularity-based-recommenders-with-polars-cc7920ad3f68/
RECOMMENDATION SYSTEM Recommender systems are algorithms designed to provide user recommendations based on their past behavior, preferences, and inter...
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https://towardsdatascience.com/building-a-recommender-system-using-machine-learning-2eefba9a692e/
The Kaggle Blueprints Welcome to the first edition of a new article series called "The [Kaggle](https://www.kaggle.com/) Blueprints", where we will an...
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Writing an LLM from scratch, part 22 -- finally training our LLM!
https://www.gilesthomas.com/2025/10/llm-from-scratch-22-finally-training-our-llm
Writing an LLM from scratch, part 22 -- finally training our LLM! This post wraps up my notes on chapter 5 of Sebastian Raschka's book "Build a Large ...
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GitHub - KazukiOnodera/Instacart: 2nd place solutionπŸ₯•πŸ₯ˆ
https://github.com/KazukiOnodera/Instacart
I made two models for predicting reorder & None. Following are the features I made. - How often the user reordered items - Time between orders - Time ...
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TimesFM: Time Series Foundation Model for time-series forecasting (github.com/google-research)
https://news.ycombinator.com/item?id=40297946
I'm curious why we seem convinced that this is a task that is possible or something worthy of investigation. I've worked on language models since 2018...
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Estimating from No Data: Deriving a Continuous Score from Categories
https://towardsdatascience.com/estimating-from-no-data-deriving-a-continuous-score-from-categories/
It has proven trivial to train a neural net to predict one of the three outcomes from the 8 features with almost complete accuracy. However, the healt...
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