TEMPORAL CONVOLUTION Meaning and
Definition
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Temporal convolution refers to a mathematical operation that combines two signals, typically in the field of signal processing or machine learning, to produce a new signal. It involves the use of a convolutional neural network (CNN), also known as a convolutional network, which is a type of deep learning architecture designed to process data with a grid-like structure, such as images or time-series data.
In the context of time-series data, such as audio signals or stock market prices, temporal convolution operates by applying a convolution operation to a sliding window of the input signal. This sliding window, known as a kernel or filter, moves over the input signal and performs a mathematical operation that captures both local and global temporal features. By convolving the kernel with the input signal, the convolution operation computes a new output signal, with each element of the output signal representing a higher-level representation of the input data.
The purpose of temporal convolution is to extract meaningful patterns and relationships within the time-series data, enabling the network to learn and recognize important temporal features, such as trends, dynamics, or periodicity. This operation plays a crucial role in deep learning applications, especially in tasks like speech recognition, gesture recognition, video analysis, and natural language processing, where the temporal aspect of the data is crucial for accurate prediction or classification.
Overall, temporal convolution is a fundamental operation in deep learning that allows for the extraction of relevant temporal features from time-series data, facilitating the understanding and processing of complex sequential information.
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A practical medical dictionary. By Stedman, Thomas Lathrop. Published 1920.
Common Misspellings for TEMPORAL CONVOLUTION
Etymology of TEMPORAL CONVOLUTION
The word "temporal convolution" is composed of two parts: "temporal" and "convolution".
The term "temporal" is derived from the Latin word "tempus", meaning "time". It refers to something related to time, in this case, the temporal aspect of signal processing or data analysis.
The word "convolution" has its roots in the Latin word "convolvere", which means "to roll or to wrap together". Convolution is a mathematical operation that combines two functions to produce a third. In signal processing, it is often used to analyze the relationship between two signals or to modify one signal based on another.
When combined, "temporal convolution" refers to the process of applying convolution in the context of time and signal analysis. It is commonly used in areas such as image processing, audio processing, and natural language processing, where the temporal aspects of data are crucial in understanding and analyzing patterns and relationships.
