Jax's repositories
PracticalGuidetoRecSys
《互联网大厂推荐算法实战》资料库
Ai-Learn
人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域
awesome-chatgpt-prompts-zh
ChatGPT 中文调教指南。各种场景使用指南。学习怎么让它听你的话。
Awesome-Chinese-LLM
整理开源的中文大语言模型，以规模较小、可私有化部署、训练成本较低的模型为主，包括底座模型，垂直领域微调及应用，数据集与教程等。
Deep-Learning-with-TensorFlow-book
深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.
eat_tensorflow2_in_30_days
Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋
GraphNeuralNetwork
《深入浅出图神经网络：GNN原理解析》配套代码
jpmml-evaluator
Java Evaluator API for PMML
jpmml-xgboost
Java library and command-line application for converting XGBoost models to PMML
keras-docs-zh
Chinese (zh-cn) translation of the Keras documentation.
LeetCodeAnimation
Demonstrate all the questions on LeetCode in the form of animation.（用动画的形式呈现解LeetCode题目的思路）
Lhy_Machine_Learning
李宏毅2021春季机器学习课程课件及作业
Neural-Networks-with-Keras-Cookbook
Neural Networks with Keras Cookbook, published by Packt
nlp-tutorial
Natural Language Processing Tutorial for Deep Learning Researchers
NLP_ability
总结梳理自然语言处理工程师(NLP)需要积累的各方面知识，包括面试题，各种基础知识，工程能力等等，提升核心竞争力
nlp_tutorial
NLP超强入门指南，包括各任务sota模型汇总（文本分类、文本匹配、序列标注、文本生成、语言模型），以及代码、技巧
Recommendation-Systems-without-Explicit-ID-Features-A-Literature-Review
Paper List of Pre-trained Foundation Recommender Models
SparrowRecSys
A Deep Learning Recommender System
TakeHomeDataChallenges
My solution to the book <A collection of Data Science Take-home Challenges>
Tech_Aarticle
深度学习模型在各大公司实战落地细节解读：主要是通过阅读各种实战文章，梳理模型落地的工程细节，涉及到搜索/推荐/自然语言处理。
TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
tensorflow-in-depth
《深入理解TensorFlow》项目代码与样章
tensorflow_cookbook
Code for Tensorflow Machine Learning Cookbook
x-deeplearning
An industrial deep learning framework for high-dimension sparse data