
Lists (1)
Sort Name ascending (A-Z)
Starred repositories
Langflow is a low-code app builder for RAG and multi-agent AI applications. It’s Python-based and agnostic to any model, API, or database.
ChatGLM3 series: Open Bilingual Chat LLMs | 开源双语对话语言模型
Official implementations for various pre-training models of ERNIE-family, covering topics of Language Understanding & Generation, Multimodal Understanding & Generation, and beyond.
✨✨Latest Advances on Multimodal Large Language Models
A large-scale 7B pretraining language model developed by BaiChuan-Inc.
An open source implementation of CLIP.
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
搜索、推荐、广告、用增等工业界实践文章收集(来源:知乎、Datafuntalk、技术公众号)
transformer in tensorflow 2.0
推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.
This Repository includes recent papers (RecSys, SIGIR, WWW, etc.) related to the Recommender Systems
Source code for Twitter's Recommendation Algorithm
Evolutionary algorithm toolbox and framework with high performance for Python
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
TSP算法全复现:遗传(GA)、粒子群(PSO)、模拟退火(SA)、禁忌搜索(ST)、蚁群算法(ACO)、自自组织神经网络(SOM)
Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Ranking (CTR/CVR prediction), Post Ranking, Large Model (Generative Recommen…
推荐系统入门教程,在线阅读地址:https://datawhalechina.github.io/fun-rec/
TensorFlow implementation of multi-task learning architectures, incl. MMoE & PLE, on wechat dataset
The tensorflow implementation of GHM loss include class loss and regression loss. GHM loss is peoposed in "Gradient Harmonized Single-stage Detector" published on AAAI 2019 (Oral).
my blog https://blog.csdn.net/qq_35649669/article/details/105586099
A TensorFlow Keras implementation of "Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts" (KDD 2018)