Optimization and Regularization Methods in Deep Learning
Article n°10 of the “Deep Learning for Computer Vision” series
Deep learning, a subset of machine learning, has revolutionized the field of computer vision with its ability to perform complex tasks with high accuracy. However, training a deep learning model is not a straightforward task. It involves finding the optimal set of parameters that minimize the loss function, a process known as optimization. Additionally, to ensure that the model generalizes well to unseen data, techniques known as regularization methods are employed. In this article, I will share with you what I have learned and an in-depth understanding of these techniques from what I picked during my journey (which is still ongoing btw) and share also a practical implementation of the techniques using Python.
1. Optimization Methods
Optimization methods are algorithms used to adjust the parameters of a deep learning model to minimize the loss function. The goal is to find the best possible configuration of parameters that results in the lowest loss. You can think of these methods as different routes you can take…
