Programming with data is a paradigm in machine learning where a program's behavior is determined by presenting it with a large labeled dataset rather than explicitly writing step-by-step rules. By providing many examples of inputs and their desired outputs, the system learns the underlying patterns, effectively allowing the data itself to 'program' the model's logic for complex tasks.

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The machine learning training process is the iterative procedure of discovering the optimal parameter settings to achieve desired model behavior using data. A typical training loop involves four steps: first, initializing a model with random parameters; second, sampling a batch of data along with their corresponding labels; third, tweaking the parameters to improve the model's predictions on those specific examples; and finally, repeating the data sampling and parameter tweaking steps until the model achieves satisfactory performance.

Machine Learning Training Process

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Programming with Data

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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