What is data visualization in deep learning?

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Data visualization in deep learning refers to the process of visually representing and interpreting complex data generated during the training and evaluation of deep learning models. It involves transforming the raw data into graphical or visual formats that facilitate understanding, analysis, and communication of patterns, trends, and relationships within the data.

In deep learning, data visualization serves multiple purposes:

Model Performance Analysis: Visualizing metrics such as loss, accuracy, or validation curves helps assess the performance and behavior of deep learning models over time. These visualizations enable researchers and practitioners to identify potential issues, understand model convergence, and make informed decisions to improve model performance.
Feature Visualization: Deep learning models often learn intricate and abstract representations of data. Data visualization techniques, such as heatmaps or activation maps, can help visualize and interpret the learned features or activations within the neural network, providing insights into what the model is focusing on during inference.

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