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A TensorFlow Implementation of the Transformer: Attention Is All You Need
Source code and dataset for IJCAI 2019 paper "ProNE: Fast and Scalable Network Representation Learning"
Supplementary code and data of the paper Evaluating network embedding techniques' performances in software bug prediction
Multi-scale feature fusion with attention mechanism for software defect prediction
D-RGCN: A Software Defect Prediction Model Based on Dual Directed Dependency Graph Reconstruction
Software in C and data files for the popular GloVe model for distributed word representations, a.k.a. word vectors or embeddings
Software defect prediction based on gated hierarchical LSTMs
This repository hosts the Hybrid Feature-Driven Convolutional Bi-LSTM Model (HFDCL), a novel software defect prediction approach. The HFDCL combines traditional and semantic features, leveraging Co…
Recreation of "Deep Learning for Just-In-Time Defect Prediction"
A tool for Software Defect Prediction
✔（已完结）最全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection features
A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
A curated list of awesome Machine Learning frameworks, libraries and software.
PyTorch implementations of Generative Adversarial Networks.
A review of change detection methods, including codes and open data sets for deep learning. From paper: change detection based on artificial intelligence: state-of-the-art and challenges.
A comprehensive and up-to-date compilation of datasets, tools, methods, review papers, and competitions for remote sensing change detection.
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！