Deep Neural Network (DNN) Training Task

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A Deep Neural Network (DNN) Training Task is a multi-layer neural network training task that requires the production of a deep neural network. A Deep Neural Network (DNN) Training Task is a Multi-Layer Neural Network Training Task that requires the production of a Deep Neural Network.



References

2023

  • (GBard, 2023) ⇒ A Deep Neural Network (DNN) Training Task is the process of providing a DNN with data and instructions so that it can learn to perform a specific task. This typically involves the following steps:
    • Data Preparation:
      • Gathering and pre-processing data relevant to the task.
      • Cleaning and labeling the data to ensure its quality.
      • Splitting the data into training, validation, and testing sets.
    • Model Definition:
      • Choosing and defining the DNN architecture.
      • Selecting the appropriate activation functions, optimizers, and loss functions.
      • Defining the training parameters (e.g., learning rate, epochs).
    • Training:
      • Feeding the training data to the DNN and adjusting its parameters (weights and biases) using a chosen optimizer to minimize the loss function.
      • Monitoring the training process through validation loss and other metrics.
      • Adjusting the hyperparameters (e.g., learning rate, architecture) if needed.