Training Loop
A foundational concept in machine learning engineering referring to the control flow that drives iterative updates of model parameters, typically comprising forward propagation, loss computation, backpropagation, gradient computation, and optimizer parameter updates.
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The training loop is the control flow that drives iterative updates of model parameters, typically consisting of a loop over steps: forward propagation to compute the loss, backward propagation to compute gradients, and the optimizer updating parameters.
Together's post-training service supports developer customization of training loop components such as data loading, loss functions, and reward computation, rather than using fixed fine-tuning templates.
The training loop paradigm in machine learning includes defining a task, executing, evaluating results, adjusting based on feedback, and executing again.
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The training loop is the control flow that drives iterative updates of model parameters, typically consisting of a loop over steps: forward propagation to compute the loss, backward propagation to compute gradients, and the optimizer updating parameters.
50%UnverifiedTogether's post-training service supports developer customization of training loop components such as data loading, loss functions, and reward computation, rather than using fixed fine-tuning templates.
50%UnverifiedThe training loop paradigm in machine learning includes defining a task, executing, evaluating results, adjusting based on feedback, and executing again.
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