Practical Machine Learning with Python by Dipanjan Sarkar Raghav Bali & Tushar Sharma

Author:Dipanjan Sarkar, Raghav Bali & Tushar Sharma
Language: eng
Format: epub, pdf
Publisher: Apress, Berkeley, CA

Optimization

The final stage in the learning process is optimization. Optimization in this case can be simply described as searching through all the hypothesis model space representations, to find the one that will give us the most optimal value of our evaluation function. While this description of optimization hides the vast majority of complexities involved in the process, it is a good way to understand the core principles. The optimization method that we will normally use is dependent on the choice of representations and the evaluation function or functions. Fortunately we already have a huge set of robust optimizers we can use once we have decided on the representation and the evaluation aspects. Optimization methods can be methods like gradient descent and even meta-heuristical methods like generic algorithms.

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