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ZhangKobe's Projects

annotated_deep_learning_paper_implementations icon annotated_deep_learning_paper_implementations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

blog icon blog

Public repo for HF blog posts

caffe icon caffe

Caffe: a fast open framework for deep learning.

clip icon clip

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

clip4caption icon clip4caption

A Simple Baseline Based on CLIP and Transformer for Video Captioning

cv icon cv

✔(已完结)最全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】

d2l-zh icon d2l-zh

《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。

datasets icon datasets

🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

diffusers icon diffusers

🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.

frozen-in-time icon frozen-in-time

Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval [ICCV'21]

llm-quickstart icon llm-quickstart

Quick Start for Large Language Models (Theoretical Learning and Practical Fine-tuning) 大语言模型快速入门(理论学习与微调实战)

llm-viz icon llm-viz

3D Visualization of an GPT-style LLM

llmbind icon llmbind

LLMBind: A Unified Modality-Task Integration Framework

mmkb icon mmkb

Several data modalities for KBs (visual, numerical, temporal, etc.)

mmt icon mmt

Multi-Modal Transformer for Video Retrieval

moe-llava icon moe-llava

Mixture-of-Experts for Large Vision-Language Models

mpikgc icon mpikgc

[LREC-COLING 2024] Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models

open-sora icon open-sora

Open-Sora: Democratizing Efficient Video Production for All

openai-quickstart icon openai-quickstart

A comprehensive guide to understanding and implementing large language models with hands-on examples using LangChain for GenAI applications.

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