Popular GitHub Repositories for Android Interview Questions

? Light and Fast AI Assistant. Support: Web | iOS | MacOS | Android | Linux | Windows

AI's query engine - Platform for building AI that can learn and answer questions over large scale federated data.

Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.

Cross-platform, customizable ML solutions for live and streaming media.

ncnn is a high-performance neural network inference framework optimized for the mobile platform

Collection of various algorithms in mathematics, machine learning, computer science, physics, etc implemented in C for educational purposes.

Filament是一个实时物理渲染引擎，支持Android、iOS、Windows、Linux、macOS和WebGL2平台。

A one stop repository for generative AI research updates, interview resources, notebooks and much more!

Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node

MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba. Full multimodal LLM Android App:[MNN-LLM-Android](./apps/Android/MnnLlmChat/README.md)

fay是一个帮助数字人（2.5d、3d、移动、pc、网页）或大语言模型（openai兼容、deepseek）连通业务系统的agent框架。

主要记录大语言大模型（LLMs） 算法（应用）工程师相关的知识及面试题

深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）

High accuracy RAG for answering questions from scientific documents with citations

Speech-to-text, text-to-speech, speaker diarization, speech enhancement, and VAD using next-gen Kaldi with onnxruntime without Internet connection. Support embedded systems, Android, iOS, HarmonyOS, Raspberry Pi, RISC-V, x86_64 servers, websocket server/client, support 11 programming languages

This repo is meant to serve as a guide for Machine Learning/AI technical interviews.

Flower: A Friendly Federated AI Framework

NanoDet-Plus?Super fast and lightweight anchor-free object detection model. ?Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone?

High Performance Chinese License Plate Recognition Framework.

A helpful 5-page machine learning cheatsheet to assist with exam reviews, interview prep, and anything in-between.