In deep learning frameworks, a neural network model and its underlying parameters can be explicitly allocated to a specific hardware device, such as a GPU, for storage and computation. For example, this is achieved using `net.to(device=try_gpu())` in PyTorch, `net.initialize(ctx=try_gpu())` in MXNet, or by instantiating the model within a `strategy.scope()` in TensorFlow.

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In machine learning, parameters can be intuitively understood as adjustable 'knobs' that dictate the behavior of a flexible program. By manipulating these parameters, practitioners can change how the program maps inputs to outputs. Once these parameter values are determined and fixed, the resulting program is referred to as a model.

Machine Learning Model Parameter

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A machine learning model family represents the entire collection of distinct programs, or input-output mappings, that can be generated simply by adjusting a specific set of parameters. Choosing an appropriate model family is crucial, as different families are required to handle fundamentally different types of inputs and outputs, such as mapping audio to text versus images to captions.

Machine Learning Model Family

In machine learning, hyperparameters are user-defined, tunable settings that govern the structure or training process of a model but are not updated within the primary training loop itself. Examples include the number of epochs (`max_epochs`), the minibatch size (`batch_size`), and the learning rate (`lr`). Because these values are not optimized by the standard training algorithm along with the model's weights and biases, they must be specified beforehand. Despite not being learned during training, they still significantly influence the performance of the model, affecting both optimization during training and generalization to unseen data.

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