zhangxy-2019's repositories
TextualExplInContext
The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning (NeurIPS 2022)
Awesome-pytorch-list
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
ML2022-Spring
**Official** 李宏毅 (Hung-yi Lee) 機器學習 Machine Learning 2022 Spring
Python-100-Days
Python - 100天从新手到大师
AI-research-tools
:hammer:AI 方向好用的科研工具
nndl.github.io
《神经网络与深度学习》 邱锡鹏著 Neural Network and Deep Learning
all-of-statistics
Self-study on Larry Wasserman's "All of Statistics"
PromptPapers
Must-read papers on prompt-based tuning for pre-trained language models.
Top-AI-Conferences-Paper-with-Code
This repository is a collection of AI top conferences papers (e.g. ACL, EMNLP, NAACL, COLING, AAAI, IJCAI, ICLR, NeurIPS, and ICML) with open resource code
t-few
Code for T-Few from "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning"
Paper-Writing-Tips
Paper Writing Tips
PRML
PRML algorithms implemented in Python
pumpkin-book
《机器学习》（西瓜书）公式详解
DeepLearning
深度学习入门教程, 优秀文章, Deep Learning Tutorial
DataStructures
My chapterwise implementation effort of " Fundamentals of Data Structures In C " By Horowitz Sahani Anderson-Freed
CPT4DST
Official code for "Continual Prompt Tuning for Dialog State Tracking" (ACL 2022).
Machine-learning-learning-notes
周志华《机器学习》又称西瓜书是一本较为全面的书籍，书中详细介绍了机器学习领域不同类型的算法(例如：监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等)，记录了本人在学习过程中的理解思路与扩展知识点，希望对新人阅读西瓜书有所帮助！
UCL
Code for the paper "Representational Continuity for Unsupervised Continual Learning" (ICLR 22)
PADA
Official code for the paper "PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains".
Lhy_Machine_Learning
李宏毅2021春季机器学习课程课件及作业
GPy
Gaussian processes framework in python
LoRA
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
SL-Agent
Efficient self-learning framework for end-to-end dialog generation.
Dive-into-DL-PyTorch
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
mt-dnn
Multi-Task Deep Neural Networks for Natural Language Understanding
leeml-notes
李宏毅《机器学习》笔记，在线阅读地址：https://datawhalechina.github.io/leeml-notes
lm-human-preferences
Code for the paper Fine-Tuning Language Models from Human Preferences
pytorch-book
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)
Machine-Learning-Notes
周志华《机器学习》手推笔记