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Computational flow model

A computational flow model is a conceptual framework used primarily in the context of deep learning and artificial intelligence. It describes the system as a flow-based computation where data moves through a defined structure^[600-developer__big-data__ai__tensorflow-01.md].

Core Components

The term is derived from the combination of two key elements:

  • Tensor: Refers to the underlying data structure, specifically multidimensional arrays used to represent the data^[600-developer__big-data__ai__tensorflow-01.md].
  • Flow: Refers to the computational model or the process by which data flows through the system^[600-developer__big-data__ai__tensorflow-01.md].

This architecture forms the basis for frameworks like TensorFlow, where the "flow" metaphor describes the execution of operations across a graph of data processing nodes.

Application in Neural Networks

In the workflow of neural networks, the computational flow model facilitates a sequence of operations^[600-developer__big-data__ai__tensorflow-01.md]:

  1. Feature Extraction: Identifying and extracting specific characteristics from input entities^[600-developer__big-data__ai__tensorflow-01.md].
  2. Structure Definition: Establishing the architecture of the neural network^[600-developer__big-data__ai__tensorflow-01.md].
  3. Training: Adjusting model parameters through iterative training^[600-developer__big-data__ai__tensorflow-01.md].
  4. Prediction: Using the trained model to make predictions on new data^[600-developer__big-data__ai__tensorflow-01.md].
  • [[Deep Learning]]
  • [[Artificial Intelligence]]
  • [[Machine Learning]]
  • [[Neural Network]]

Sources

^[600-developer__big-data__ai__tensorflow-01.md]