AWS Machine Learning Blog

FEBRUARY 10, 2023

As an example, in the following figure, we separate Cover 3 Zone (green cluster on the left) and Cover 1 Man (blue cluster in the middle). We design an algorithm that automatically identifies the ambiguity between these two classes as the overlapping region of the clusters. Gomez, Łukasz Kaiser, and Illia Polosukhin.

ML 96

ML ML Machine Learning Machine Learning 96

The MLOps Blog

DECEMBER 28, 2022

Union of business and data teams The success of ML projects lies in the strong collaboration between the data team and the business team. Such continuous alliance of the business team helps the data science team to create ML models that have the potential to add significant business value.

ML 78

ML ML Data Scientist Machine Learning 78

Flipboard

NOVEMBER 24, 2023

The humble beginnings with Iris In 2017, SnapLogic unveiled Iris, an industry-first AI-powered integration assistant. Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. He currently is working on Generative AI for data integration.

Database 159

Database AWS ETL SQL 159

PyImageSearch

OCTOBER 30, 2023

Spotify also establishes a taste profile by grouping the music users often listen into clusters. These clusters are not based on explicit attributes (e.g., Figure 6: Recurrent neural networks (source: Venkatachalam, 2019, Towards Data Science ). genre, artist, etc.) but rather on the compositional similarity of songs.

K-nearest Neighbors 70

K-nearest Neighbors Algorithm Clustering Machine Learning 70

Explosion

AUGUST 3, 2017

Data science projects are said to have uneven returns, like start-ups: a minority of projects are very successful, recouping costs for a larger number of failures. Rather than spending a month figuring out an unsupervised machine learning problem, just label some data for a week and train a classifier.

Supervised Learning 52

Supervised Learning Python Machine Learning Machine Learning 52

Mlearning.ai

DECEMBER 21, 2023

MTEB Leaderboard at Hugging Face evaluates almost all available embedding models across seven use cases —  Classification, Clustering, Pair Classification, Reranking, Retrieval, Semantic Textual Similarity (STS) and Summarization. However, now they recommend ada v2 for all tasks. Another important consideration is cost.

Database 94

Database AI AI Machine Learning 94

Mlearning.ai

JUNE 6, 2023

We have the IPL data from 2008 to 2017. Most dominant colors in an image using KMeans clustering In this blog, we will find the most dominant colors in an image using the K-Means clustering algorithm, this is a very interesting project and personally one of my favorites because of its simplicity and power.

Machine Learning 52

Machine Learning Machine Learning Python Deep Learning 52

Hacker News

JANUARY 9, 2024

We’ve had the ability to do global computations about solar eclipses for some time (actually since soon before the 2017 eclipse ). One’s not dealing with some (usually very expensive) “just for PDEs” package; what we now have is a “consumerized” way to handle PDEs whenever they’re needed—for engineering, science, or whatever.

Python 181

Python Algorithm Machine Learning Machine Learning 181