The ability to Interpret image data using software is advancing fast! The two images above are captioned with program generated text. Here's an article that describes concepts and an approach to generate a caption for an image. The code is written in Python and uses TensorFlow

How to build and train an image caption generator using a TensorFlo...

"TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well." Here's a TensorFlow tutorial. 

Views: 157

Comment

You need to be a member of Codetown to add comments!

Join Codetown

Happy 10th year, JCertif!

Notes

Welcome to Codetown!

Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.

When you create a profile for yourself you get a personal page automatically. That's where you can be creative and do your own thing. People who want to get to know you will click on your name or picture and…
Continue

Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.

Looking for Jobs or Staff?

Check out the Codetown Jobs group.

 

Enjoy the site? Support Codetown with your donation.



InfoQ Reading List

Tailwind CSS 4.2 Ships Webpack Plugin, New Palettes and Logical Property Utilities

Tailwind CSS version 4.2.0, released on February 18, 2026, includes a webpack plugin for streamlined integration and four new color palettes. It expands logical property utilities and improves recompilation speed by 3.8x. This update is particularly beneficial for teams on existing projects and those developing multilingual applications.

By Daniel Curtis

Cloudflare and ETH Zurich Outline Approaches for AI-Driven Cache Optimization

Cloudflare and ETH Zurich highlight how AI-driven crawler traffic challenges traditional caching in CDNs and databases. They propose AI-aware strategies including separate cache tiers, adaptive algorithms, and pay-per-crawl models to balance performance for human users and AI services while maintaining cache efficiency and system stability.

By Leela Kumili

GitHub Actions Custom Runner Images Reach General Availability

GitHub has just announced the availability of custom images for its hosted runners. They've finally left the public preview phase that started back in October behind them. This feature will enable teams to use a GitHub-approved base image and then construct a virtual machine image that really meets their workflow requirements.

By Claudio Masolo

Presentation: Local First – How To Build Software Which Still Works After the Acquihire

Alex Good discusses the fragility of modern cloud-dependent apps and shares a roadmap for "local-first" software. By leveraging a Git-like DAG structure and Automerge, he explains how to move from brittle client-server models to resilient systems where data lives on-device. He explores technical implementation, rich-text merging, and how this infrastructure simplifies engineering workflows.

By Alex Good

Article: Stateful Continuation for AI Agents: Why Transport Layers Now Matter

Agent workflows make transport a first-order concern. Multi-turn, tool-heavy loops amplify overhead that is negligible in single-turn LLM use. Stateful continuation cuts overhead dramatically. Caching context server-side can reduce client-sent data by 80%+ and improve execution time by 15–29% .

By Anirudh Mendiratta

© 2026   Created by Michael Levin.   Powered by

Badges  |  Report an Issue  |  Terms of Service