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https://github.com/facebookresearch/segment-anything
Please check out our new release on Segment Anything Model 2 (SAM 2).
- SAM 2 code: https://github.com/facebookresearch/segment-anything-2
- SAM 2 demo: https://sam2.metademolab.com/
- SAM 2 paper: https://arxiv.org/abs/2408.00714
Segment Anything Model 2 (SAM 2) is a foundation model towards solvin...
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https://ai.meta.com/blog/segment-anything-foundation-model-image-segmentation/
April 5, 2023
Segmentation — identifying which image pixels belong to an object — is a core task in computer vision and is used in a broad array of applications, from analyzing scientific imagery to editing photos. But creating an accurate segmentation model for specific tasks typically requires hig...
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https://towardsdatascience.com/a-framework-for-building-a-production-ready-feature-engineering-pipeline-f0b29609b20f/
The Full Stack 7-Steps MLOps Framework
This tutorial represents lesson 1 out of a 7-lesson course that will walk you step-by-step through how to design, implement, and deploy an ML system using MLOps good practices. During the course, you will build a production-ready model to forecast energy consum...
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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 recommendation algorithms for implicit feedback datasets:
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Alternating Least Squares as described in the papers Collaborative Filtering for Implicit Feedback Dat...
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https://levelup.gitconnected.com/exploratory-data-analysis-the-ultimate-workflow-a82b1d21f747
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Exploratory Data Analysis: The Ultimate Workflow
Explore the true potential of your data with Python
Are you tired of starting from scratch every time you need to explore your data, without a clear roadmap? Look no further!
I will guide you through a step-by-step process using Pyth...
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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 Amazon, Netflix, or Spotify, telling you what to buy, watch or listen to next. While recommender systems are convenient for us users – we get inspired...
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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. With the massive growth of available online contents, users have been inundated with choices. It is therefore crucial for web platforms to offer reco...
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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 analyze Kaggle competitions’ top solutions for lessons we can apply to our own data science projects.
This first edition will review the techniques and ...
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https://towardsdatascience.com/the-portfolio-that-got-me-a-data-scientist-job-513cc821bfe4/
Getting a Data Scientist job is hard.
This isn’t 2015 anymore: it’s not enough to know a few pandas functions and put the words "Big Data" on your résumé. Competition for the top jobs is fierce. On a recent trawl through the LinkedIn jobs board, I struggled to find a London-based Data Scientist role...
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https://towardsdatascience.com/time-series-forecasting-deep-learning-vs-statistics-who-wins-c568389d02df/
In recent years, Deep Learning has made remarkable progress in the field of NLP.
Time series, also sequential in nature, raise the question: what happens if we bring the full power of pretrained transformers to time-series forecasting?
However, some papers, such as [2] and [3] have scrutinized Deep ...
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https://towardsdatascience.com/a-performant-recommender-system-without-cold-start-problem-69bf2f0f0b9b/
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 nothing more but a vector of numbers. The intuition is that the dot product of the user and the item ...
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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 interactions. Becoming integral to various industries, including e-commerce, entertainment, and advertising, recommender systems improve user experience, i...
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https://mattgosden.medium.com/setting-up-a-home-gpu-deep-learning-server-the-easy-way-da27bdccef6a
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Setting up a home GPU deep-learning server (the easiest way for Mac users)
A practical step-by-step guide
This article outlines how to turn an old desktop PC with a GPU into a remote GPU server for doing deep learning or other tasks. This works well in my setup where I want to do a...
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https://medium.com/@mustafaakin/indexing-icloud-photos-with-ai-using-llava-and-pgvector-fd58182febf6
Indexing iCloud Photos with AI Using LLaVA and pgvector
A straightforward idea, gluing stuff together until it works, but it’s a glimpse of what’s possible with help of local AIs in near future
I’ve been fascinated about the rise of AI. However, for the most of the part, it feels like magic. I like ...
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Video Surveillance with YOLO+llava (github.com/psychip)
I've been using Frigate for a long time and it's a really cool project that has been quite reliable. The configuration can be a little bit of a headache to learn, but it gets better with every release.
Viserion is new to me though, that looks really cool.
I've been running frigate for a while now an...
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https://spin.atomicobject.com/redis-postgresql/
There’s a tried-and-true architecture that I’ve seen many times for supporting your web services and applications:
- PostgreSQL for data storage
- Redis for coordinating background job queues (and some limited atomic operations)
Redis is fantastic, but what if I told you that its most common use cas...
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https://matt.blwt.io/post/7-databases-in-7-weeks-for-2025/
Matt Blewitt
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https://leontrolski.github.io/postgres-as-queue.html
2024-02-09
The team I've been working in for the last year or so have had great success using Postgres-as-queue. We've managed to avoid the following:
In a nut shell, it's simpler - there are just way fewer moving parts.
As we're using a monolithic codebase with a reasonable ORM, we also have none o...
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https://www.moment.dev/blog/lies-i-was-told-pt-1
In early 2024, I began investigating collaborative editing systems for use in Moment’s core text editor.
In some ways, we are in a golden era for the problem. Several algorithms now claim to solve not only the online case (where many people simultaneously edit a document), but also the offline case ...
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https://www.prisma.io/dataguide/postgresql/inserting-and-modifying-data/insert-on-conflict
PostgreSQL / Inserting and modifying data
How to use `INSERT ON CONFLICT` to upsert data in PostgreSQL
Introduction
PostgreSQL lets you either add or modify a record within a table depending on whether the record already exists. This is commonly known as an "upsert" operation (a portmanteau of "inse...
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