A sequence-to-sequence model, also known as an encoder-decoder model, is a deep learning model that handles variable-length input and output sequences. It consists of two parts: an encoder for processing the input...
Generative models are a class of machine learning models that learn the distribution of data and generate new samples. They create new instances that are similar to real-world data but have never appeared before by capturing patterns in the training dataset. ...
A Bayesian Network, also known as a belief network or probabilistic directed acyclic graph, is a probabilistic graphical model. It uses directed edges and nodes (representing random variables) to graphically represent the probabilistic dependencies between variables...
Domain adaptation is a method in transfer learning that aims to transfer a model from one domain (source domain) to another different domain (target domain), improving the model's performance in the target domain. Mainly...
Federated learning is a distributed machine learning technique that allows multiple participants to collaboratively train a model without sharing the original data. In this way, parties can protect data privacy and...
Embedded learning is an innovative educational model that seamlessly integrates the learning process into daily work and activities. Embedded learning posits that when learning content is closely related to work, employee learning...
AI ethics is a discipline that studies the ethical issues and risks involved in the development and application of artificial intelligence technologies. It focuses on ensuring that the design, development, and deployment of AI systems comply with ethical standards, promoting fairness, transparency, and ethical conduct.
Automated Machine Learning (AutoML) is a set of tools and techniques that automate certain tasks in a machine learning workflow, such as data preprocessing, feature engineering, model selection, hyperparameter optimization, etc.
Q-learning in reinforcement learning is a model-free reinforcement learning algorithm that finds the optimal policy by learning an action-value function Q(s, a). Given a state, Q(s, a) represents the expected total future reward for performing action a...
Neural style transfer uses deep learning to apply the visual style of one image to the content of another, creating a new image that blends the characteristics of both. It is based on pre-trained convolutional...
Semantic segmentation is a computer vision technique that uses deep learning models, especially convolutional neural networks (CNNs), to classify each pixel in an image, recognizing and distinguishing different objects...
Object detection is a key technology in computer vision, involving the identification and localization of one or more target objects from images or videos. It requires not only determining the presence of a specific object in an image, but also...
Speech recognition (ASR), also known as automatic speech recognition, is a high-tech process that converts human speech into text or commands. Through steps such as feature extraction, pattern matching, and model training, it enables machines to...
Text generation is a technique in the field of Natural Language Processing (NLP) that enables computers to automatically create readable and grammatically correct text. It involves converting non-linguistic information (such as data or images) into text, or...
Adversarial attacks are a type of security threat where attackers intentionally add subtle perturbations to the input data, causing machine learning models, especially deep learning models, to make incorrect predictions or actions...
Fuzzy logic is a mathematical method for handling uncertainty and fuzziness. It mimics the human brain's thought process, supporting logical values that lie between absolute true (1) and false (0). It expresses these values through membership functions...
Artificial intelligence security (AI Security) refers to taking necessary measures to protect artificial intelligence systems from attacks, intrusions, interference, and illegal use, ensuring their stable and reliable operation, and adhering to principles such as human-centeredness and consistency of rights and responsibilities to safeguard human rights...
Swarm intelligence is a distributed intelligent algorithm that simulates the collective behavior of biological groups in nature. By mimicking the collective behavior of social organisms such as ants and bees, it utilizes simple local interactions between individuals to generate...
Edge computing is a distributed computing architecture that moves data processing and analysis tasks from centralized data centers to the network edge, closer to the data source and the user. This can significantly reduce data transmission...
Exploration strategies refer to the action plans an agent takes when facing an unknown environment, in order to discover behaviors with higher rewards. These strategies help the agent "explore" new possibilities that may bring higher returns...
Neural network pruning is a model compression technique that reduces model size and computational complexity by removing unimportant weights or neurons from a neural network, thereby improving runtime efficiency. It is divided into...
Sequence generation models are a class of deep learning models focused on handling problems where both input and output are sequential data. They learn the temporal dependencies and patterns within the sequence to generate new sequences...
Chain of Thought (CoT) is a groundbreaking advancement in the field of artificial intelligence. By simulating the human thought process when solving problems, it endows machines with deeper logical reasoning abilities. This is particularly relevant when faced with multi-step logical reasoning...
Speech synthesis is a technology that converts text information into spoken language. It simulates the human pronunciation mechanism by analyzing text content, extracting language features, and then using acoustic models to convert them into sound signals, ultimately...