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Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
面向开发者的 LLM 入门教程，吴恩达大模型系列课程中文版
《开源大模型食用指南》针对中国宝宝量身打造的基于Linux环境快速微调（全参数/Lora）、部署国内外开源大模型（LLM）/多模态大模型（MLLM）教程
Interview = 简历指南 + 算法题 + 八股文 + 源码分析
My blogs and code for machine learning. http://cnblogs.com/pinard
本项目是一个面向小白开发者的大模型应用开发教程，在线阅读地址：https://datawhalechina.github.io/llm-universe/
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
PyTorch入门教程，在线阅读地址：https://datawhalechina.github.io/thorough-pytorch/
主要存储Datawhale组队学习中“数据挖掘/机器学习”方向的资料。
Synthetic data generators for tabular and time-series data
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）
iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data
📚 专门为自然语言处理(NLP)面试准备的学习笔记与资料
Few Shot Learning by Siamese Networks, using Keras.
Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview que…
Directed Acyclic Tabular GAN (DATGAN) for integrating expert knowledge in synthetic tabular data generation
This repo contains PyTorch implementation of cGAN, cWGAN, and cWGAN-gp for tabular data.
Code for the article "DATGAN: Integrating Expert Knowledge into Deep Learning for Synthetic Tabular Data"
This repo contains the CTGAN, which is a generative adversarial network for generating synthetic tabular data specifically call detail records
本赛题旨在运用有效的金融科技和大数据系统，分析涉赌涉诈资金交易新方式，持续优化风险监测模型，通过赛题提供的涉赌涉诈黑名单、白名单及用于训练的相关交易流水数据集，构建涉赌涉诈账户算法识别模型，全面排查存量风险。A榜排名11/1594，B榜排名13/1594。