Sequence modeling is a modeling method in natural language processing and time series analysis used to process data with sequential dependencies. It can capture the temporal or sequential relationships between data points, such as in text...
Collaborative filtering is a recommender system technique that predicts items a user might like by analyzing the similarity between users or user ratings of items. This technique can be based on user (...
A knowledge graph is a structured semantic knowledge base that graphically represents the relationships between entities. It consists of nodes (representing entities) and edges (representing the relationships between entities), and can store and process...
Embedding vectors are a technique for mapping data (such as text, images, and user information) to numerical vectors in a high-dimensional space. They can capture the similarities and relationships between data points, allowing similar data points to be mapped...
Adversarial training is a machine learning technique designed to improve a model's robustness against malicious attacks. By introducing small perturbations that may cause misclassification during training, the model learns to recognize...
Weakly supervised learning is a machine learning paradigm designed to address the problem of incomplete or inaccurate labeled data. It includes incomplete supervision (partially labeled data), imprecise supervision (data is not fully labeled), and so on.
A recurrent neural network (RNN) is a type of neural network with internal memory. This memory allows the network to process and analyze sequential data by considering information from previous steps.
Embedding representations are the process of mapping entities (such as words, images, or users) to a continuous vector space that captures the entities' intrinsic properties and relationships. In natural language processing...
LSTM, short for Long Short-Term Memory, was proposed by Sepp Hochreiter and Jürgen Schmidhuber in 1997. It is a network technology capable of remembering long-term information and using it for future computations...
A chatbot is a computer program that simulates human conversation, using Natural Language Processing (NLP) technology to understand user input and provide automated responses. It is widely used in customer service, online interaction, and information services, and can...
Diffusion models are a type of deep generative model that simulates the diffusion process of data from an ordered state to a disordered state, and conversely, the reverse diffusion process from a disordered state back to an ordered state.
Knowledge Graph Question Answering (KGQA) is a system that combines knowledge graphs and natural language processing technologies, aiming to retrieve and generate accurate answers from structured knowledge graphs by understanding users' natural language questions.
Vector databases are database systems specifically designed for storing and querying vectors. They are used to represent multi-dimensional data points, such as data used in machine learning and artificial intelligence. In a vector database, data is represented as vectors, which...
Data mining is the process of discovering patterns and other valuable information from large datasets. It utilizes machine learning and statistical analysis techniques to extract useful information from massive amounts of data, helping organizations make more informed decisions...
Retrieval-Augmented Generation (RAG) is a technique that combines Information Retrieval (IR) and Natural Language Generation (NLG). It enhances the generation of natural language by retrieving relevant information from external knowledge bases...
End-to-end learning is a design methodology in machine learning and deep learning that allows models to learn the desired output directly from the input data, without manually splitting the task into multiple independent parts...
Overfitting refers to a phenomenon where a model performs too perfectly on training data, making it unable to accurately predict new data. It occurs when the model is too complex or the training time is too long, causing the model to begin learning incorrectly...
A digital twin is a virtual digital copy of a physical entity or system that accurately reflects the state and behavior of its physical counterpart through real-time data updates. Digital twins span the entire lifecycle of an object, utilizing...
Optical Character Recognition (OCR) is a technology that converts text images into machine-readable formats. Through automatic data extraction, it can quickly recognize scanned documents, camera images, and image PDFs...
Word embedding is a technique used in Natural Language Processing (NLP) to represent text data. It maps words or phrases to a fixed-dimensional vector space, allowing semantically similar words to be represented in the same vector space...
Robotics is an interdisciplinary field that combines engineering, computer science, and design to develop, build, and apply robots. These intelligent machines can perform a variety of tasks, including but not limited to automation...
Attention mechanisms are a technique in deep learning that mimics human visual attention, enabling models to identify and focus on processing the most important parts of the input data. This mechanism is used in image recognition...
Artificial intelligence (AI) is a branch of computer science that encompasses the theories, methods, technologies, and applications of simulating and extending human intelligence. Through techniques such as machine learning and natural language processing, it aims to...
Decision trees construct a tree-like model by recursively selecting the attributes that best distinguish the data. Each node represents an attribute test, each branch represents the test result, and the leaf nodes represent the decision output. It utilizes information gain, gain ratio, or other metrics...