Stars
CV算法岗知识点及面试问答汇总，主要分为计算机视觉、机器学习、图像处理和 C++基础四大块，一起努力向offers发起冲击！
【LLMs九层妖塔】分享 LLMs在自然语言处理（ChatGLM、Chinese-LLaMA-Alpaca、小羊驼 Vicuna、LLaMA、GPT4ALL等）、信息检索（langchain）、语言合成、语言识别、多模态等领域（Stable Diffusion、MiniGPT-4、VisualGLM-6B、Ziya-Visual等）等 实战与经验。
AIGC-interview/CV-interview/LLMs-interview面试问题与答案集合仓，同时包含工作和科研过程中的新想法、新问题、新资源与新项目
本次实训按照指导书要求，设计并实现了一个简单的图像检索系统，实现了从颜色特征、纹理特征、形状特征三个方面对图像进行匹配。其中颜色特征是提取自RGB颜色空间的颜色矩；纹理特征是来自图像0度、45度、90度、135度四个角度的灰度共生矩阵的纹理一致性、纹理对比度、纹理熵，纹理相关性的期望和标准差；形状特征是采用形状不变矩法提取了图像Hu不变矩u1~u7和离心率e。在图像匹配方面采用了欧氏距离和余…
基于内容的图像检索系统（Content Based Image Retrieval,简称 CBIR）
Semantic-Forward Relaying: A Novel Framework Towards 6G Cooperative Communications
The Project focuses on designing NOMA Uplink \& Downlink System using Successive Interference Cancellation (SIC) which manages multiple user transmissions in shared time-frequency resources efficie…
Simulation code for the comparitive analysis of the number of SICs increase with the number of increase in devices in NOMA, and cooperative NOMA types
Tensorflow implementation of SICNet: a deep learning-based successive interference cancellation (SIC) receiver for non-orthogonal downlink systems
A MATLAB implementation of an OFDM based Power Domain NOMA System
Implementation of "Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel" paper (ICC 2023)
Power Allocation In NOMA
The fastest knowledge base for growing teams. Beautiful, realtime collaborative, feature packed, and markdown compatible.
深入探索精选的套壳站和必备API资源。本文为初学者和经验丰富的运营者提供一站式指南，涵盖常见问题解答和基础攻略，助您迈向套壳站副业成功之路。Dive into a curated selection of shell sites and essential APIs. This article offers a comprehensive guide for both beginners a…
Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step
A implement of Deep JSCC for wireless image transmission by PyTorch
A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)
🦜🔗 Build context-aware reasoning applications
Universal LLM Deployment Engine with ML Compilation
Python code for IEEE TVT: Two-Way Semantic Communications without Feedback (Semantic Communication)
Multi-User Semantic Communication for Visual Question Answering
Channel-aware GAN Inversion for Semantic Communication