Liang Xie's Projects
Anomaly detection related books, papers, videos, and toolboxes
accessible AutoML for deep learning.
Awesome-LLM: a curated list of Large Language Model
A curated list of awesome machine learning interpretability resources.
Lightweight Linux for Docker
Causal Inference for the Brave and True的中文翻译版。全部代码基于Python,适用于计量经济学、量化社会学、策略评估等领域。英文版原作者:Matheus Facure
Course notes for CS224N Winter17
Public facing notes page
cuML - RAPIDS Machine Learning Library
Data science interview questions and answers
《数据科学工程实践》一书的Jupyter Notebook库,以及交流园地。
Deep NLP Course
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
The fastai deep learning library, plus lessons and and tutorials
Machine learning, computer vision, statistics and general scientific computing for .NET. I am working on TimeSeries library.
Generalized linear mixed-effect model in Python
Fit interpretable models. Explain blackbox machine learning.
Book about interpretable machine learning
Practical techniques for interpreting machine learning models.
Build a Jekyll blog in minutes, without touching the command line.
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.
Welcome to Keras Deep Learning on Graphs (Keras-DGL) http://vermaMachineLearning.github.io/keras-deep-graph-learning
Run Keras models in the browser, with GPU support using WebGL
Everything I practice about keras for deep learning
Utilities for working with image data, text data, and sequence data.
Neural network visualization toolkit for keras
⚡ Building applications with LLMs through composability ⚡