<ul><li>Optimisation techniques are algorithms and methods used to adjust the parameters of a model to minimise the difference between the predictions and the actual values.</li><li>Feature scaling involves transforming the range of input variables to ensure that they are on the same scale.</li><li>Batch normalisation helps to stabilise the learning process and allows for the use of higher learning rates.</li><li>Different optimisation techniques like mini-batch gradient descent, momentum, RMSProp, Adam, and learning rate decay have their own advantages and disadvantages.</li></ul>

Deep Learning 101: Optimisation in Machine Learning

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