mdjabc Goto Github PK

ai-roadmap

ApacheCN AI 路线图（知识树）

amazon-sagemaker-examples

Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker

anchor

Code for "High-Precision Model-Agnostic Explanations" paper

anomaly-detection-resources

Anomaly detection related books, papers, videos, and toolboxes

autogbt-alt

An experimental Python package that reimplements AutoGBT using LightGBM and Optuna.

awesome-ensemble-learning

Ensemble learning related books, papers, videos, and toolboxes

awesome-interpretable-machine-learning

awesome-machine-learning-interpretability

A curated list of awesome machine learning interpretability resources.

awesome-meta-learning

A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.

awesome-public-datasets

A topic-centric list of HQ open datasets. PR ☛☛☛

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

awesome-transfer-learning

Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)

bayesian-optimization

Code and Example for Bayesian Optimization for LightGBM

bayesian-optimization-1

Python code for bayesian optimization using Gaussian processes

breast-cancer-detection-using-deep-belief-network-from-scratch

DBN (deep belief network) implementation for breast cancer detection

broad-learning-system

BLS Code

broad-learning-system-1

catboost

Gradient Boosting with CatBoost.

catboost_graphviz

show tree struct of catboost model by graphviz

classification

Classification model including XGBoost, GBDT, RandomForest, lightGBM, stacking model...

classification-with-catboost-and-shap

I use CatBoost to predict which Amazon employees should get access to certain data and use SHAP to visualise and explain why a certain employee was rejected or granted access.

clustergcn

A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).

convnet-interpretability-keras

Visualizing VGG16 Convolutional Neural Network using Keras

convnet_transfer

Code for paper "How transferable are features in deep neural networks?"

data-mining

数据挖掘的相关案例和demo

deep-belief-network

A Python implementation of Deep Belief Networks built upon NumPy and TensorFlow with scikit-learn compatibility

deep-transfer-learning-crop-prediction

Deep transfer learning techniques for crop yield prediction, published in COMPASS 2018. Best Note Winner.

deeplearning

深度学习入门教程&&优秀文章&&Deep Learning Tutorial

deeplearning-models

A collection of various deep learning architectures, models, and tips

deeplearning_tutorials

The deeplearning algorithms implemented by tensorflow