Hidden layers are intermediate layers in artificial neural networks, located between the input and output layers. Their function is to extract and transform features from the input data, providing high-level features for the final output layer. Hidden layers...
The Transformer model is a deep learning model first proposed in 2017 by a Google research team in their paper "Attention is All You Need". The core idea of this model is the self-attention mechanism...
Model quantization is a key technique in deep learning model optimization. It reduces the storage and computational requirements of the model by decreasing the bit width of its parameters, thereby improving the model's performance on various hardware platforms...
NeRF (Neural Radiance Fields) is a computer vision technique for generating high-quality 3D reconstruction models. It uses deep learning techniques to extract the geometric shape and texture information of objects from images viewed from multiple perspectives, and then...
Facial recognition is a biometric technology that identifies individuals based on their facial features. It automatically detects and tracks faces by acquiring images or video streams containing human faces, and further...
3D Gaussian Splatting is an emerging 3D scene reconstruction and rendering technique. It uses 3D Gaussian functions to represent points in a scene and projects these Gaussian functions onto a 2D image plane...
Contextual embedding is a technique that maps words to vector spaces, generating a representation for each word based on its context. These representations capture the diversity of words in different contexts...
Regularization is a technique used in machine learning to prevent overfitting. It's achieved by adding a penalty term to the model's loss function, which is related to the complexity of the model parameters. Regularization helps limit...
Data augmentation is a common technique in machine learning and deep learning, widely used in computer vision and natural language processing. Its core purpose is to transform or generate new data...
Foundation models are a type of model that has rapidly developed in the field of artificial intelligence in recent years. They are pre-trained on large-scale, widely sourced datasets and can perform a range of general tasks. These models are based on...
Synthetic data is data that is not artificially created; it is generated through computational algorithms and simulations to mimic real-world data. It possesses the same mathematical properties as actual data, but does not contain the same specific details...
Generative Adversarial Networks (GANs) are deep learning models consisting of a generator and a discriminator. They learn through a game-like interaction to generate data. The generator's task is to produce data from random noise, while the discriminator...
Supervised fine-tuning (SFT) is the process of further training a pre-trained model using a labeled dataset specific to a particular task. Pre-trained models are typically trained on large-scale unsupervised datasets...
Cross-validation is an important model validation technique in statistical analysis, primarily used to evaluate a model's generalization ability on unknown data. It involves splitting the dataset into several subsets and then using different methods to cross-validate the data...
Fine-tuning is a process of making minor adjustments to the parameters of a pre-trained model for a specific task or dataset. This process fully utilizes the general characteristics learned by the pre-trained model on large-scale datasets...
Semantic understanding is an important area of Natural Language Processing (NLP), focusing on understanding and interpreting the meaning of words, phrases, and sentences in text. It involves literal meaning, but also includes context,...
Knowledge representation refers to representing knowledge in a machine-understandable form. It involves a combination of data structures and processing mechanisms: Representation = Data Structure + Processing Mechanism. In knowledge representation...
Meta-learning, also known as "learning to learn," is an exciting and promising research area in machine learning. Its core goal is to enable machine learning systems to efficiently learn new tasks and solve...
Video Diffusion Models (VDM) are generative models that combine the advantages of variational autoencoders (VAEs) and diffusion models. The core idea of VDM is to perform the diffusion process in the latent space, rather than...
Hyperparameter tuning is a crucial step in machine learning model training, involving selecting the optimal combination of hyperparameters to optimize model performance. Hyperparameters are configurations set before the learning process begins...
Real-Time Engagement (RTE) is a collection of technologies, product architectures, and scenario solutions designed to enable two parties communicating and interacting remotely to realistically and seamlessly recreate the offline interaction scenario in real time...
Robotic Process Automation (RPA) is a software technology that automates business processes by simulating human behavior and interacting with digital systems and software. RPA software robots can...
Mixture of Experts (MoE) is a technique in machine learning used to build large models by decomposing them into multiple sub-networks or "experts" to improve model performance and efficiency. Each expert...
Genetic algorithms (GA) are heuristic search algorithms that simulate the process of biological evolution, belonging to the category of evolutionary algorithms. They are based on the ideas of natural selection and genetics, simulating the process of natural selection...