Search Results for "ai mini projects"

Bloxtor is a **professional and intelligent web-development framework designed for No-Coders, Low-Coders, and Coders alike**. It functions as a versatile code reader and converter, seamlessly transforming code into visual environments and visual components back into code. The concept is to provide a centralized AI-powered web development platform with multiple levels of Citizen Development, empowering both technical and non-technical developers to easily create any type of web application,...

A framework that allows small programs to support componentized development. One of the most popular small program framework. Component development, perfect solution to the problems of component isolation, component nesting, component communication, etc. Supports the use of third-party npm resources, automatically handles dependencies between npm resources, and is perfectly compatible with all npm resource packages without platform dependencies. Through polyfill, the mini program perfectly...

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research. MMF is designed from ground up to let you focus on what matters, your model, by providing boilerplate code for distributed training, common datasets and state-of-the-art pre-trained baselines out-of-the-box.

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine...

...Caffe also offers great speed, capable of processing over 60M images per day with a single NVIDIA K40 GPU. It’s arguably one of the fastest convnet implementations around. Caffe is developed by the Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and a great community of contributors that continue to make Caffe state-of-the-art in both code and models. It’s been used in numerous projects, from startup prototypes and academic research projects, to large scale industrial applications.

The ERDialog project offers a simple browser framework for small projects written in Erlang. Written applets include a bittorrent client, address book, text editor, AI experiments and graphics and music applications.