#kmeans — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #kmeans, aggregated by home.social.
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От текста к смыслу: Embeddings, GPT и многомерные векторы в конкурентном анализе мобильных приложений
Отзывы пользователей — один из самых ценных источников информации о продукте, при этом часто клиенты описывают одну и ту же тему или проблему десятками разных слов. Раньше работать с фидбэком было долго и ресурсоемко, но с появлением Embeddings и LLM это изменилось.
https://habr.com/ru/companies/garage8/articles/1057100/
#Embeddings #GPT #LLM #OpenAI_Embeddings_API #анализ_отзывов #кластеризация_отзывов #KMeans #Agglomerative_Clustering #тональность_отзывов #voice_of_customer
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От текста к смыслу: Embeddings, GPT и многомерные векторы в конкурентном анализе мобильных приложений
Отзывы пользователей — один из самых ценных источников информации о продукте, при этом часто клиенты описывают одну и ту же тему или проблему десятками разных слов. Раньше работать с фидбэком было долго и ресурсоемко, но с появлением Embeddings и LLM это изменилось.
https://habr.com/ru/companies/garage8/articles/1057100/
#Embeddings #GPT #LLM #OpenAI_Embeddings_API #анализ_отзывов #кластеризация_отзывов #KMeans #Agglomerative_Clustering #тональность_отзывов #voice_of_customer
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От текста к смыслу: Embeddings, GPT и многомерные векторы в конкурентном анализе мобильных приложений
Отзывы пользователей — один из самых ценных источников информации о продукте, при этом часто клиенты описывают одну и ту же тему или проблему десятками разных слов. Раньше работать с фидбэком было долго и ресурсоемко, но с появлением Embeddings и LLM это изменилось.
https://habr.com/ru/companies/garage8/articles/1057100/
#Embeddings #GPT #LLM #OpenAI_Embeddings_API #анализ_отзывов #кластеризация_отзывов #KMeans #Agglomerative_Clustering #тональность_отзывов #voice_of_customer
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От PDF к учебному модулю: практичный ML-пайплайн внутри LMS
Всем привет, с вами Михаил Киселев, ML-разработчик в компании WebRise. И сегодня поговорим о практическом применении ML в образовании. Почему при горе регламентов, инструкций и методичек запуск нового курса всё равно растягивается на недели? И почему проблема часто не в LMS, а на шаг раньше — там, где знания в компании уже есть, а учебной структуры ещё нет?
https://habr.com/ru/articles/1053450/
#ml #lms #tfidf #kmeans #python #дистанционное_образование #дистанционное_обучение
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От PDF к учебному модулю: практичный ML-пайплайн внутри LMS
Всем привет, с вами Михаил Киселев, ML-разработчик в компании WebRise. И сегодня поговорим о практическом применении ML в образовании. Почему при горе регламентов, инструкций и методичек запуск нового курса всё равно растягивается на недели? И почему проблема часто не в LMS, а на шаг раньше — там, где знания в компании уже есть, а учебной структуры ещё нет?
https://habr.com/ru/articles/1053450/
#ml #lms #tfidf #kmeans #python #дистанционное_образование #дистанционное_обучение
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От PDF к учебному модулю: практичный ML-пайплайн внутри LMS
Всем привет, с вами Михаил Киселев, ML-разработчик в компании WebRise. И сегодня поговорим о практическом применении ML в образовании. Почему при горе регламентов, инструкций и методичек запуск нового курса всё равно растягивается на недели? И почему проблема часто не в LMS, а на шаг раньше — там, где знания в компании уже есть, а учебной структуры ещё нет?
https://habr.com/ru/articles/1053450/
#ml #lms #tfidf #kmeans #python #дистанционное_образование #дистанционное_обучение
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PTS ϟ 13th Birthday @ Strange Brew - 02 May feat. Evian Christ, k means, Nono Gigsta + more
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K-means clustering is a simple and widely used method for identifying patterns in data.
I also use it in a recent Statistics Globe Hub module, where it is combined with synthetic data created using the drawdata Python library: https://statisticsglobe.com/hub
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K-means clustering is a simple and widely used method for identifying patterns in data.
I also use it in a recent Statistics Globe Hub module, where it is combined with synthetic data created using the drawdata Python library: https://statisticsglobe.com/hub
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K-means clustering is a simple and widely used method for identifying patterns in data.
I also use it in a recent Statistics Globe Hub module, where it is combined with synthetic data created using the drawdata Python library: https://statisticsglobe.com/hub
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K-means clustering is a simple and widely used method for identifying patterns in data.
I also use it in a recent Statistics Globe Hub module, where it is combined with synthetic data created using the drawdata Python library: https://statisticsglobe.com/hub
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K-means clustering is a simple and widely used method for identifying patterns in data.
I also use it in a recent Statistics Globe Hub module, where it is combined with synthetic data created using the drawdata Python library: https://statisticsglobe.com/hub
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I’ve just published a new module in the Statistics Globe Hub on how to draw synthetic datasets using the drawdata Python library and analyze them afterward in R using k-means clustering.
More information about the Statistics Globe Hub: https://statisticsglobe.com/hub
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I’ve just published a new module in the Statistics Globe Hub on how to draw synthetic datasets using the drawdata Python library and analyze them afterward in R using k-means clustering.
More information about the Statistics Globe Hub: https://statisticsglobe.com/hub
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I’ve just published a new module in the Statistics Globe Hub on how to draw synthetic datasets using the drawdata Python library and analyze them afterward in R using k-means clustering.
More information about the Statistics Globe Hub: https://statisticsglobe.com/hub
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I’ve just published a new module in the Statistics Globe Hub on how to draw synthetic datasets using the drawdata Python library and analyze them afterward in R using k-means clustering.
More information about the Statistics Globe Hub: https://statisticsglobe.com/hub
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I’ve just published a new module in the Statistics Globe Hub on how to draw synthetic datasets using the drawdata Python library and analyze them afterward in R using k-means clustering.
More information about the Statistics Globe Hub: https://statisticsglobe.com/hub
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🤖: Spoiler Alert! Flash-KMeans promises to be a "memory-efficient" magic trick, unless you count the mental gymnastics required to understand it. 🤯 Just what the world needs, another K-Means #variant to make your brain cells do a triple axel! 🧠💥
https://arxiv.org/abs/2603.09229 #FlashKMeans #MemoryEfficient #KMeans #DataScience #MachineLearning #AI #HackerNews #ngated -
🤖: Spoiler Alert! Flash-KMeans promises to be a "memory-efficient" magic trick, unless you count the mental gymnastics required to understand it. 🤯 Just what the world needs, another K-Means #variant to make your brain cells do a triple axel! 🧠💥
https://arxiv.org/abs/2603.09229 #FlashKMeans #MemoryEfficient #KMeans #DataScience #MachineLearning #AI #HackerNews #ngated -
🤖: Spoiler Alert! Flash-KMeans promises to be a "memory-efficient" magic trick, unless you count the mental gymnastics required to understand it. 🤯 Just what the world needs, another K-Means #variant to make your brain cells do a triple axel! 🧠💥
https://arxiv.org/abs/2603.09229 #FlashKMeans #MemoryEfficient #KMeans #DataScience #MachineLearning #AI #HackerNews #ngated -
🤖: Spoiler Alert! Flash-KMeans promises to be a "memory-efficient" magic trick, unless you count the mental gymnastics required to understand it. 🤯 Just what the world needs, another K-Means #variant to make your brain cells do a triple axel! 🧠💥
https://arxiv.org/abs/2603.09229 #FlashKMeans #MemoryEfficient #KMeans #DataScience #MachineLearning #AI #HackerNews #ngated -
🤖: Spoiler Alert! Flash-KMeans promises to be a "memory-efficient" magic trick, unless you count the mental gymnastics required to understand it. 🤯 Just what the world needs, another K-Means #variant to make your brain cells do a triple axel! 🧠💥
https://arxiv.org/abs/2603.09229 #FlashKMeans #MemoryEfficient #KMeans #DataScience #MachineLearning #AI #HackerNews #ngated -
@zefu I find the tool works best for images with a decent contrast and/or color hue range. I also recommend not choosing more than 5-8 colors to avoid too many similar ones. Also bear in mind that k-means clustering relies on random initializations and so running the process multiple times for the same image can lead to slightly different results (just press "update" a few times and see if there're any decent changes)...
Another tip: I personally like having palettes which also include some desaturated colors, so try reducing the "min chroma" slider value (a change will recompute automatically). If you only want more rich colors, then bump up the value, but it all really very much depends on the image... The two variations attached here use min chroma 5 and 0...
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@zefu I find the tool works best for images with a decent contrast and/or color hue range. I also recommend not choosing more than 5-8 colors to avoid too many similar ones. Also bear in mind that k-means clustering relies on random initializations and so running the process multiple times for the same image can lead to slightly different results (just press "update" a few times and see if there're any decent changes)...
Another tip: I personally like having palettes which also include some desaturated colors, so try reducing the "min chroma" slider value (a change will recompute automatically). If you only want more rich colors, then bump up the value, but it all really very much depends on the image... The two variations attached here use min chroma 5 and 0...
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@zefu I find the tool works best for images with a decent contrast and/or color hue range. I also recommend not choosing more than 5-8 colors to avoid too many similar ones. Also bear in mind that k-means clustering relies on random initializations and so running the process multiple times for the same image can lead to slightly different results (just press "update" a few times and see if there're any decent changes)...
Another tip: I personally like having palettes which also include some desaturated colors, so try reducing the "min chroma" slider value (a change will recompute automatically). If you only want more rich colors, then bump up the value, but it all really very much depends on the image... The two variations attached here use min chroma 5 and 0...
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@zefu I find the tool works best for images with a decent contrast and/or color hue range. I also recommend not choosing more than 5-8 colors to avoid too many similar ones. Also bear in mind that k-means clustering relies on random initializations and so running the process multiple times for the same image can lead to slightly different results (just press "update" a few times and see if there're any decent changes)...
Another tip: I personally like having palettes which also include some desaturated colors, so try reducing the "min chroma" slider value (a change will recompute automatically). If you only want more rich colors, then bump up the value, but it all really very much depends on the image... The two variations attached here use min chroma 5 and 0...
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@zefu I find the tool works best for images with a decent contrast and/or color hue range. I also recommend not choosing more than 5-8 colors to avoid too many similar ones. Also bear in mind that k-means clustering relies on random initializations and so running the process multiple times for the same image can lead to slightly different results (just press "update" a few times and see if there're any decent changes)...
Another tip: I personally like having palettes which also include some desaturated colors, so try reducing the "min chroma" slider value (a change will recompute automatically). If you only want more rich colors, then bump up the value, but it all really very much depends on the image... The two variations attached here use min chroma 5 and 0...
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@zefu I should update the readme to explain how these palettes were created. They're a manually curated selection of running hundreds of images through this tool (doesn't look like much, but it's been super helpful over the years) and then handpicking my favorites:
https://demo.thi.ng/umbrella/dominant-colors/
This uses k-means clustering for segmentation, also available as library:
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@zefu I should update the readme to explain how these palettes were created. They're a manually curated selection of running hundreds of images through this tool (doesn't look like much, but it's been super helpful over the years) and then handpicking my favorites:
https://demo.thi.ng/umbrella/dominant-colors/
This uses k-means clustering for segmentation, also available as library:
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@zefu I should update the readme to explain how these palettes were created. They're a manually curated selection of running hundreds of images through this tool (doesn't look like much, but it's been super helpful over the years) and then handpicking my favorites:
https://demo.thi.ng/umbrella/dominant-colors/
This uses k-means clustering for segmentation, also available as library:
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@zefu I should update the readme to explain how these palettes were created. They're a manually curated selection of running hundreds of images through this tool (doesn't look like much, but it's been super helpful over the years) and then handpicking my favorites:
https://demo.thi.ng/umbrella/dominant-colors/
This uses k-means clustering for segmentation, also available as library:
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@zefu I should update the readme to explain how these palettes were created. They're a manually curated selection of running hundreds of images through this tool (doesn't look like much, but it's been super helpful over the years) and then handpicking my favorites:
https://demo.thi.ng/umbrella/dominant-colors/
This uses k-means clustering for segmentation, also available as library:
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🚀 Excited to announce version 0.6 of my kmeans package for #gretl is out!
✨ New in this release:Added silhouette analysis support with three new functions. These help you assess cluster quality. Accessible via scripting and GUI!
🔗 Help text + sample : https://gretl.sourceforge.net/current_fnfiles/kmeans.gfn
Background:
https://en.wikipedia.org/wiki/Silhouette_(clustering)RT @gretl
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HyAB k-means for color quantization
https://30fps.net/pages/hyab-kmeans/
#HackerNews #HyAB #k-means #color #quantization #colorquantization #machinelearning #dataanalysis #kmeans
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HyAB k-means for color quantization
https://30fps.net/pages/hyab-kmeans/
#HackerNews #HyAB #k-means #color #quantization #colorquantization #machinelearning #dataanalysis #kmeans
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HyAB k-means for color quantization
https://30fps.net/pages/hyab-kmeans/
#HackerNews #HyAB #k-means #color #quantization #colorquantization #machinelearning #dataanalysis #kmeans
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HyAB k-means for color quantization
https://30fps.net/pages/hyab-kmeans/
#HackerNews #HyAB #k-means #color #quantization #colorquantization #machinelearning #dataanalysis #kmeans
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HyAB k-means for color quantization
https://30fps.net/pages/hyab-kmeans/
#HackerNews #HyAB #k-means #color #quantization #colorquantization #machinelearning #dataanalysis #kmeans
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Recently I've combined various functions which I've been using in other projects (e.g. my personal PKM toolchain) and published them as new library https://thi.ng/text-analysis for better re-use:
- customizable, composable & extensible tokenization (transducer based)
- ngram generation
- Porter-stemming & stopword removal
- vocabulary (bi-directional index) creation
- dense & sparse multi-hot vector encoding/decoding
- histograms (incl. sorted versions)
- tf-idf (term frequency & inverse document frequency), multiple strategies
- k-means clustering (with k-means++ initialization & customizable distance metrics)
- similarity/distance functions (dense & sparse versions)
- central terms extractionThe attached code example (also in the project readme) uses this package to creeate a clustering of all ~210 #ThingUmbrella packages, based on their assigned tags/keywords...
The library is not intended to be a full-blown NLP solution, but I keep on finding myself running into these functions/concepts quite often, and maybe you'll find them useful too...
#Text #Analysis #Cluster #KMeans #TFIDF #Ngram #Vector #TypeScript #JavaScript
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Recently I've combined various functions which I've been using in other projects (e.g. my personal PKM toolchain) and published them as new library https://thi.ng/text-analysis for better re-use:
- customizable, composable & extensible tokenization (transducer based)
- ngram generation
- Porter-stemming & stopword removal
- vocabulary (bi-directional index) creation
- dense & sparse multi-hot vector encoding/decoding
- histograms (incl. sorted versions)
- tf-idf (term frequency & inverse document frequency), multiple strategies
- k-means clustering (with k-means++ initialization & customizable distance metrics)
- similarity/distance functions (dense & sparse versions)
- central terms extractionThe attached code example (also in the project readme) uses this package to creeate a clustering of all ~210 #ThingUmbrella packages, based on their assigned tags/keywords...
The library is not intended to be a full-blown NLP solution, but I keep on finding myself running into these functions/concepts quite often, and maybe you'll find them useful too...
#Text #Analysis #Cluster #KMeans #TFIDF #Ngram #Vector #TypeScript #JavaScript
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Recently I've combined various functions which I've been using in other projects (e.g. my personal PKM toolchain) and published them as new library https://thi.ng/text-analysis for better re-use:
- customizable, composable & extensible tokenization (transducer based)
- ngram generation
- Porter-stemming & stopword removal
- vocabulary (bi-directional index) creation
- dense & sparse multi-hot vector encoding/decoding
- histograms (incl. sorted versions)
- tf-idf (term frequency & inverse document frequency), multiple strategies
- k-means clustering (with k-means++ initialization & customizable distance metrics)
- similarity/distance functions (dense & sparse versions)
- central terms extractionThe attached code example (also in the project readme) uses this package to creeate a clustering of all ~210 #ThingUmbrella packages, based on their assigned tags/keywords...
The library is not intended to be a full-blown NLP solution, but I keep on finding myself running into these functions/concepts quite often, and maybe you'll find them useful too...
#Text #Analysis #Cluster #KMeans #TFIDF #Ngram #Vector #TypeScript #JavaScript
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Recently I've combined various functions which I've been using in other projects (e.g. my personal PKM toolchain) and published them as new library https://thi.ng/text-analysis for better re-use:
- customizable, composable & extensible tokenization (transducer based)
- ngram generation
- Porter-stemming & stopword removal
- vocabulary (bi-directional index) creation
- dense & sparse multi-hot vector encoding/decoding
- histograms (incl. sorted versions)
- tf-idf (term frequency & inverse document frequency), multiple strategies
- k-means clustering (with k-means++ initialization & customizable distance metrics)
- similarity/distance functions (dense & sparse versions)
- central terms extractionThe attached code example (also in the project readme) uses this package to creeate a clustering of all ~210 #ThingUmbrella packages, based on their assigned tags/keywords...
The library is not intended to be a full-blown NLP solution, but I keep on finding myself running into these functions/concepts quite often, and maybe you'll find them useful too...
#Text #Analysis #Cluster #KMeans #TFIDF #Ngram #Vector #TypeScript #JavaScript
-
Recently I've combined various functions which I've been using in other projects (e.g. my personal PKM toolchain) and published them as new library https://thi.ng/text-analysis for better re-use:
- customizable, composable & extensible tokenization (transducer based)
- ngram generation
- Porter-stemming & stopword removal
- vocabulary (bi-directional index) creation
- dense & sparse multi-hot vector encoding/decoding
- histograms (incl. sorted versions)
- tf-idf (term frequency & inverse document frequency), multiple strategies
- k-means clustering (with k-means++ initialization & customizable distance metrics)
- similarity/distance functions (dense & sparse versions)
- central terms extractionThe attached code example (also in the project readme) uses this package to creeate a clustering of all ~210 #ThingUmbrella packages, based on their assigned tags/keywords...
The library is not intended to be a full-blown NLP solution, but I keep on finding myself running into these functions/concepts quite often, and maybe you'll find them useful too...
#Text #Analysis #Cluster #KMeans #TFIDF #Ngram #Vector #TypeScript #JavaScript
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Playing around with features for the next version of #chromamagic. Producing a reduced palette is trickier than you might think to make something useful for painters. #octtree #kmeans #imagequantization #oilpainting #watercolor
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Playing around with features for the next version of #chromamagic. Producing a reduced palette is trickier than you might think to make something useful for painters. #octtree #kmeans #imagequantization #oilpainting #watercolor
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Playing around with features for the next version of #chromamagic. Producing a reduced palette is trickier than you might think to make something useful for painters. #octtree #kmeans #imagequantization #oilpainting #watercolor
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Playing around with features for the next version of #chromamagic. Producing a reduced palette is trickier than you might think to make something useful for painters. #octtree #kmeans #imagequantization #oilpainting #watercolor
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Master K-means Clustering with Rand Index and Adjusted Rand Score. Unlock valuable insights into your data by grouping and evaluating points for maximum clarity. #kmeans #clustering
https://teguhteja.id/k-means-clustering-master-rand-index-adjusted-rand-score/
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Master K-means Clustering with Rand Index and Adjusted Rand Score. Unlock valuable insights into your data by grouping and evaluating points for maximum clarity. #kmeans #clustering
https://teguhteja.id/k-means-clustering-master-rand-index-adjusted-rand-score/
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Master K-means Clustering with Rand Index and Adjusted Rand Score. Unlock valuable insights into your data by grouping and evaluating points for maximum clarity. #kmeans #clustering
https://teguhteja.id/k-means-clustering-master-rand-index-adjusted-rand-score/
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Машинное обучение: Кластеризация методом K-means. Теория и реализация. С нуля
Здравствуйте, дорогие читатели. В этой статье я приведу разбор того, как работает метод кластеризации К-средних на низком уровне. Содержание: идея метода, как присваивать метки неразмеченным объектам, реализация на чистом Python и разбор кода.
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Машинное обучение: Кластеризация методом K-means. Теория и реализация. С нуля
Здравствуйте, дорогие читатели. В этой статье я приведу разбор того, как работает метод кластеризации К-средних на низком уровне. Содержание: идея метода, как присваивать метки неразмеченным объектам, реализация на чистом Python и разбор кода.
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Машинное обучение: Кластеризация методом K-means. Теория и реализация. С нуля
Здравствуйте, дорогие читатели. В этой статье я приведу разбор того, как работает метод кластеризации К-средних на низком уровне. Содержание: идея метода, как присваивать метки неразмеченным объектам, реализация на чистом Python и разбор кода.
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Как анализировать тысячи отзывов с ChatGPT? Частые ошибки и пример на реальных данных
В этой статье я расскажу про свой опыт решения рабочей задачи — анализ отзывов о компании от пользователей. Мы разберем возможные ошибки и посмотрим на пример кода и реальных данных. Гайд будет полезен всем, у кого нет большого опыта в анализе данных или работе с LLM через API.
https://habr.com/ru/articles/821287/
#llm #gpt #chatgpt #python #clustering #kmeans #tsne #visualization #summarization #data_analysis
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Как анализировать тысячи отзывов с ChatGPT? Частые ошибки и пример на реальных данных
В этой статье я расскажу про свой опыт решения рабочей задачи — анализ отзывов о компании от пользователей. Мы разберем возможные ошибки и посмотрим на пример кода и реальных данных. Гайд будет полезен всем, у кого нет большого опыта в анализе данных или работе с LLM через API.
https://habr.com/ru/articles/821287/
#llm #gpt #chatgpt #python #clustering #kmeans #tsne #visualization #summarization #data_analysis