iorikyo79 Goto Github PK

amc_project

automl_lectures

autotrain-advanced

🤗 AutoTrain Advanced

awesome-chatgpt-prompts

This repo includes ChatGPT prompt curation to use ChatGPT better.

awesome-segment-anything-medical-images

awesome-SAM-Medical-Images

cppclean

Finds problems in C++ source that slow development of large code bases

cs231n

CS231n 번역 프로젝트

embedchain

Framework to easily create LLM powered bots over any dataset.

generative-ai-for-beginners

12 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

gpt-researcher

GPT based autonomous agent that does online comprehensive research on any given topic

langchain-full-course

langserve_ollama

무료로 한국어🇰🇷 파인튜닝 모델 받아서 로컬 LLM 호스팅. LangServe, Ollama, streamlit + RAG

learnablepromptsam

Try to use the SAM-ViT as the backbone to create the learnable prompt for semantic segmentation

midl2021-vindr-ribcxr

VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays

mnist

openai-cookbook

Examples and guides for using the OpenAI API

openchat

Run and create custom ChatGPT-like bots with OpenChat, embed and share these bots anywhere, the open-source chatbot console.

optimizedcppstudy

2017년 상반기에 진행하는 Optimized C++ 스터디 관련 자료입니다.

paper

Paper 정리

pinecone_examples

prompt-engineering-guide

🐙 Guides, papers, lecture, notebooks and resources for prompt engineering

promptbase

All things prompt engineering

qlora

QLORA: Efficient Finetuning of Quantized LLMs

roop

one-click deepfake (face swap)

sam-remove-background

Extract objection and remove background application using Segment Anything Model(SAM).

samexporter

Export Segment Anything Models to ONNX

segment-anything-cpp-wrapper

skills-introduction-to-github

My clone repository

summarize_youtube_video_lecture

Summarize Youtube's script by chapter creater configured

tensorflow-mnist-mlp-batch_normalization-weight_initializers

MNIST classification using Multi-Layer Perceptron (MLP) with 2 hidden layers. Some weight-initializers and batch-normalization are implemented.