Alvaro Pang's repositories
100-Days-Of-ML-Code
100 Days of ML Coding
100-Interview-for-Deeplearning
百面深度学习-学习
2021-Interview_Reference
2021年最新总结，阿里，腾讯，百度，美团，头条等技术面试题目，以及答案，专家出题人分析汇总。
aigc
《构筑大语言模型应用：应用开发与架构设计》一本关于 LLM 在真实世界应用的开源电子书，介绍了大语言模型的基础知识和应用，以及如何构建自己的模型。其中包括Prompt的编写、开发和管理，探索最好的大语言模型能带来什么，以及LLM应用开发的模式和架构设计。
AutoTickets
This is a code that uses computer programs to assist ticket purchase. The first goal is to make entertainment activities such as concerts and music festivals.
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。
BigGNNer
This is a manual implementation of some GNN graph neural network related code library
bioinformatics_pipeline
Bioinformatics Pipeline
coding.everyday
This project is used to record the disassembly process and execution results of daily programming questions.
DAEGC
Deep Attention Embedding Graph Clustering(19IJCAI)
PyTorch-Examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Data-Science-Complete-Tutorial
Notebooks to learn data science
DeepLog
Anomaly detection is a critical step towards building a secure and trustworthy system. The primary purpose of a system log is to record system states and significant events at various critical points to help debug system failures and perform root cause analysis. Such log data is universally available in nearly all computer systems. Log data is an important and valuable resource for understanding system status and performance issues; therefore, the various system logs are naturally excellent source of information for online monitoring and anomaly detection. We propose DeepLog, a deep neural network model utilizing Long Short-Term Memory (LSTM), to model a system log as a natural language sequence. This allows DeepLog to automatically learn log patterns from normal execution, and detect anomalies when log patterns deviate from the model trained from log data under normal execution.
DockerPractice
Learn and understand Docker technologies, with real DevOps practice!
GAN-keras
tensorflow2.x implementations of Generative Adversarial Networks.
Golang_Practice
Source Code for Go In Action examples
gpt-engineer
Specify what you want it to build, the AI asks for clarification, and then builds it.
Hugging-LLM
HuggingLLM, Hugging Future.
Interview-Notebook
:books: 技术面试需要掌握的基础知识整理，欢迎编辑~
iRF
iterative Random Forests (iRF): iteratively grows weighted random forests, finds interaction among features
IT-eBOOK
所有内容仅为编程练习使用，内容抓取自互联网，如有侵权，将及时清理删除
MachineLearning
Machine learning resources，including algorithm, paper, dataset, example and so on.
MetaboAnalystR
R package for MetaboAnalyst
MicrobiomeHelper
A repository of bioinformatic scripts, SOPs, and tutorials for analyzing microbiome data.
newcoding_by_java
Daily algorithm exercise
Statistical_Learning_Methods
Implementation of Statistical Learning Method, Second Edition.《统计学习方法》第二版，算法实现。
Tensorflow2_Tutorials_Chinese
tensorflow2中文教程，持续更新(当前版本:tensorflow2.0)，tag: tensorflow 2.0 tutorials
xzpang.github.io
This project is my personal homepage, mainly used to record learning notes and related information sharing.