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Regularization is the process of shrinking or regularising the coefficients towards zero in machine learning. To put it another way, regularisation prevents overfitting by discouraging the learning of a more complicated or flexible model.

The goal of optimising an objective function is to identify a set of inputs that results in a maximum or minimal function evaluation. Many machine learning algorithms, from fitting logistic regression models to training artificial neural networks, are based on this difficult topic.

One of the most fundamental topics in machine learning is regularisation. It's a method of preventing the model from overfitting by providing additional data. When using training data, the machine learning model may perform well, but when using test data, it may not.

Regularization Techniques in Deep Learning = minimises or eliminates the problem of overfitting. Keras-Tuner optimises neural network structures by reducing the number of connections and neurons for best performance.

When fitting a machine learning algorithm, function optimization is the reason for minimising error, cost, or loss. In a predictive modelling project, optimization is also done during data preparation, hyperparameter tweaking, and model selection.

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