Using optimizers
At the heart of DL lies the optimization problem: finding the best set of model parameters (weights and biases) that minimize a chosen loss function. Optimization algorithms play a pivotal role in this journey by iteratively adjusting these parameters to reduce errors between predictions and actual target values.
Optimization is a fundamental concept in mathematics that refers to the process of finding the best or most favorable solution among a set of possible solutions. In the context of ML and DL, optimization is used to adjust model parameters to minimize a cost, objective, or loss function (all used interchangeably), leading to improved model performance. We have already covered that the gradient descent algorithm is used for optimization. However, there are different versions of the algorithm, and when constructing your NN, you can choose which of them to use.
Let’s consider some key aspects of optimization:
- Objective function: Optimization...
