Introduction

Introduction

Introduction

Feature decoupling is a technique used in machine learning and feature engineering to improve the performance and interpretability of models by separating correlated or redundant features into independent components. The main goal of feature decoupling is to simplify the representation of data while retaining the most relevant information for the task at hand.

Introduction

Dual learning is a paradigm in machine learning that leverages the duality between two related tasks to improve learning efficiency and performance. The basic idea of dual learning is to train two models simultaneously, each focusing on a different aspect of the problem, while using feedback from one model to improve the learning of the other.

Introduction

Contrastive learning is a machine learning technique used for representation learning, where the goal is to learn useful representations of data points by contrasting them with each other. The basic idea is to encourage similar data points to be closer to each other in the learned representation space while pushing dissimilar data points further apart.

Introduction

Explainable Artificial Intelligence (XAI) refers to the set of techniques and methodologies aimed at making the outputs and decisions of AI systems understandable to humans. The goal of XAI is to increase transparency, trust, and accountability in AI systems by providing insights into how they arrive at their conclusions.

Introduction

PyMesh is a powerful Python library for processing and manipulating 3D meshes. It provides various functionalities for working with triangular meshes, including mesh generation, manipulation, Boolean operations, remeshing, and more. PyMesh is useful for tasks in computer graphics, computational geometry, and scientific computing, allowing users to perform operations like mesh simplification, subdivision, smoothing, and exporting mesh data to various formats. If you’re interested in 3D mesh processing within Python, PyMesh can be a great tool to explore.

Introduction

FreeSurfer is a software suite used for the analysis and processing of neuroimaging data, particularly focusing on structural brain MRI scans. It’s an open-source software package developed by the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital and Harvard Medical School.

Introduction

當評估機器學習模型時，常見的指標可以用簡單的方式解釋：
這些指標可以幫助了解模型在不同問題類型中的表現，從而選擇最適合的模型以及改進模型效能。

Introduction

Federated AI Technology Enabler (FATE) is an open-source computing framework designed for federated learning scenarios. It aims to support privacy protection and secure computation in federated learning environments.

Federated learning is a decentralized machine learning approach that allows training models across multiple devices or data sources without centralizing the data in one location.

FATE provides a means for participants to conduct model training and inference while preserving data privacy.