Random walk is a statistical model consisting of a series of random action trajectories, which is used to represent irregular changes.

Neural Machine Translation (NMT) is a machine translation framework based on pure neural networks. It uses neural networks to achieve end-to-end translation from source language to target language.

The Neural Turing Machine is a Turing Machine based on a neural network. It is inspired by the Turing Machine and can implement a machine algorithm for differential functions. It includes a neural network controller and external memory.

The same strategy means that the strategy for generating samples is the same as the strategy used when the network updates parameters. A typical example of the same strategy method is the SARAS algorithm.

Receiver Operating Characteristic (ROC) is a test indicator of a system matching algorithm. It is a relationship between the matching score threshold, false positive rate, and rejection rate. It reflects the balance between the rejection rate and false positive rate of the recognition algorithm at different thresholds.

Restricted Boltzmann machine is a kind of random neural network model with two-layer structure, symmetrical connection and no self-feedback.

Simultaneous Localization and Mapping (SLAM) is a technique used in robotics.

Statistical learning is a discipline that builds probabilistic statistical models based on data to predict and analyze data, also known as statistical machine learning.

The alternative loss function is a function used when the original loss function is inconvenient to calculate.

Upsampling, or image interpolation, is mainly used to enlarge the original image so that it can be displayed on a higher resolution display device.

The vanishing gradient problem is a problem encountered when training artificial neural networks using gradient descent and backpropagation.

T-Distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning method for dimensionality reduction.

Treebank is a deep-processed corpus that performs word segmentation, part-of-speech tagging, and syntactic structure relationship tagging on sentences.

Turing machine, also known as deterministic Turing machine, is an abstract computing model proposed by Alan Turing in 1936. Its more abstract meaning is a mathematical logic machine, which can be regarded as the ultimate powerful logic machine equivalent to any finite logical mathematical process.

Specialization is a process from general to specific

A synonym set is a collection of words with the same meaning.

The time step defines how small the time intervals between physics simulations are. In a game engine, this reflects how often a function needs to run.

Parameter adjustment refers to the act of adjusting parameters in order to obtain better results.

Numerical attributes are a type of attribute that quantitatively describes data, meaning that the data is a measurable quantity.

General artificial intelligence refers to an intelligent entity with the same or superior capabilities as humans. It is also called strong artificial intelligence, which can display all intelligent behaviors of normal humans. In order to distinguish it from the AI term of traditional artificial intelligence or mainstream artificial intelligence, a general prefix is added.

Attribute space: The space formed by attributes is also called "sample space" or "input space". Feature space: The attribute space formed by the selected attributes after excluding linear correlation and attributes that are not beneficial to model construction is called feature space. Related concepts Data set […]

The naive Bayes classifier uses the "attribute conditional independence assumption": for known categories, it is assumed that all attributes are independent of each other. Improved naive Bayes: In order to prevent the information carried by other attributes from being "erased" by attribute values that have never appeared in the training set, "smoothing" is usually performed when estimating probability values, and the "Laplace correction" is often used; for […]

Generative adversarial networks are an unsupervised learning method that is implemented by letting two neural networks compete with each other. This method was proposed by Ian Goodfellow in 2014. Generative adversarial networks include a generative network and a discriminative network. The generative network takes random samples in the latent space as input, and the output needs to imitate the training […]

In machine learning, generative models can be used to directly model data or to establish conditional probability distributions between variables. Conditional probability distributions can be based on generative models of Bayes’ theorem. Generative models are suitable for unsupervised tasks such as classification and clustering. Typical generative models include the following: Gaussian mixture models and other mixed […]

Random walk is a statistical model consisting of a series of random action trajectories, which is used to represent irregular changes.

Neural Machine Translation (NMT) is a machine translation framework based on pure neural networks. It uses neural networks to achieve end-to-end translation from source language to target language.

The Neural Turing Machine is a Turing Machine based on a neural network. It is inspired by the Turing Machine and can implement a machine algorithm for differential functions. It includes a neural network controller and external memory.

The same strategy means that the strategy for generating samples is the same as the strategy used when the network updates parameters. A typical example of the same strategy method is the SARAS algorithm.

Receiver Operating Characteristic (ROC) is a test indicator of a system matching algorithm. It is a relationship between the matching score threshold, false positive rate, and rejection rate. It reflects the balance between the rejection rate and false positive rate of the recognition algorithm at different thresholds.

Restricted Boltzmann machine is a kind of random neural network model with two-layer structure, symmetrical connection and no self-feedback.

Simultaneous Localization and Mapping (SLAM) is a technique used in robotics.

Statistical learning is a discipline that builds probabilistic statistical models based on data to predict and analyze data, also known as statistical machine learning.

The alternative loss function is a function used when the original loss function is inconvenient to calculate.

Upsampling, or image interpolation, is mainly used to enlarge the original image so that it can be displayed on a higher resolution display device.

The vanishing gradient problem is a problem encountered when training artificial neural networks using gradient descent and backpropagation.

T-Distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning method for dimensionality reduction.

Treebank is a deep-processed corpus that performs word segmentation, part-of-speech tagging, and syntactic structure relationship tagging on sentences.

Turing machine, also known as deterministic Turing machine, is an abstract computing model proposed by Alan Turing in 1936. Its more abstract meaning is a mathematical logic machine, which can be regarded as the ultimate powerful logic machine equivalent to any finite logical mathematical process.

Specialization is a process from general to specific

A synonym set is a collection of words with the same meaning.

The time step defines how small the time intervals between physics simulations are. In a game engine, this reflects how often a function needs to run.

Parameter adjustment refers to the act of adjusting parameters in order to obtain better results.

Numerical attributes are a type of attribute that quantitatively describes data, meaning that the data is a measurable quantity.

General artificial intelligence refers to an intelligent entity with the same or superior capabilities as humans. It is also called strong artificial intelligence, which can display all intelligent behaviors of normal humans. In order to distinguish it from the AI term of traditional artificial intelligence or mainstream artificial intelligence, a general prefix is added.

Attribute space: The space formed by attributes is also called "sample space" or "input space". Feature space: The attribute space formed by the selected attributes after excluding linear correlation and attributes that are not beneficial to model construction is called feature space. Related concepts Data set […]

The naive Bayes classifier uses the "attribute conditional independence assumption": for known categories, it is assumed that all attributes are independent of each other. Improved naive Bayes: In order to prevent the information carried by other attributes from being "erased" by attribute values that have never appeared in the training set, "smoothing" is usually performed when estimating probability values, and the "Laplace correction" is often used; for […]

Generative adversarial networks are an unsupervised learning method that is implemented by letting two neural networks compete with each other. This method was proposed by Ian Goodfellow in 2014. Generative adversarial networks include a generative network and a discriminative network. The generative network takes random samples in the latent space as input, and the output needs to imitate the training […]

In machine learning, generative models can be used to directly model data or to establish conditional probability distributions between variables. Conditional probability distributions can be based on generative models of Bayes’ theorem. Generative models are suitable for unsupervised tasks such as classification and clustering. Typical generative models include the following: Gaussian mixture models and other mixed […]