Search Results for "machine learning projects"

CSS-Inspiration is a living gallery of modern CSS effects and components that highlights what’s possible with pure CSS and minimal markup. The collection ranges from micro-interactions and loaders to 3D transforms, complex backgrounds, hover states, and text effects, each presented as a small, self-contained demo. Source code is front and center, encouraging developers to study, copy, and tweak snippets for their own projects. Many examples lean on contemporary features—custom properties,...

My life just got a lot busier; I'd really like a maintainer or two to help. I'm not abandoning JPT, I just don't have a ton of time to put into hacking on it. If you've been learning Ruby and you want to move beyond tutorials and throwaway projects, I'd love to hear from you. I'd be happy to help you gain experience and credibility, if you're willing to help me maintain this project! It's simple to throw a photo on a page and call it a day, but doing justice to users on all different browsers and devices is tedious and tricky. ...

...It’s not only a code resource but also a design showcase, blending engineering and artistry. By presenting small, focused experiments, flutter_vignettes encourages experimentation and learning while illustrating Flutter’s strengths in rapid UI prototyping.

...GitHub Pages doesn't support custom Jekyll plug-ins, is this plug-in whitelisted? This is all written in Liquid so need for any whitelisting. Worried about how weird or ugly a plug-in's CSS may be? These projects only generate semantically sane HTML for you to style. No need to worry about learning Ruby to edit a plug-in or getting your edits approved. It’s all Liquid, just change whatever HTML you want generated.

The tensorflow-haskell package provides Haskell-language bindings for TensorFlow, giving Haskell developers the ability to build and run computation graphs, machine learning models, and leverage TensorFlow's ecosystem—though it is not an official Google release. As an expedient we use docker for building. Once you have docker working, the following commands will compile and run the tests. Run the install_macos_dependencies.sh script in the tools/ directory. The script installs dependencies via Homebrew and then downloads and installs the TensorFlow library on your machine under /usr/local. ...