Improving an Artificial Neural Network with Regularization and Optimization

Improving an Artificial Neural Network with Regularization and Optimization

In this article, we will discuss regularization and optimization techniques that are used by programmers to build a more robust and generalized neural network. We will study the most effective regularization techniques like L1, L2, Early Stopping, and Drop out which help for model generalization.

In this article, we will discuss regularization and optimization techniques that are used by programmers to build a more robust and generalized neural network. We will study the most effective regularization techniques like L1, L2, Early Stopping, and Drop out which help for model generalization. We will take a deeper look at different optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, AdaGrad, and AdaDelta for better convergence of the neural networks.

Regularization for Model Generalization

Overfitting and underfitting are the most common problems that programmers face while working with deep learning models. A model that is well generalized to data is considered to be an optimal fit for the data. The problem of overfitting occurs when the model captures the noise of data. Precisely, overfitting occurs when a learning model has low bias and high variance. While in the case of underfitting the learning model can’t capture the inherent nature of data. The problem of underfitting persists when the model does not fit well on to the data. The underfitting problem reflects low variance and high bias.

Regularization, in the context of neural networks, is a process of preventing a learning model from getting overfitted over training data. It involves a mechanism to reduce generalization errors of the learning model. Look at the following image which shows underfitting, which depicts the inability of a learning model to capture the inherent nature of data. This results in erroneous outcomes for unseen data. Also, we see overfitting over training data in the following image. This image also shows an optimum fit that presents the ability of a learning model to predict correct output for previously not seen data.

neural-networks regularization optimization artificial-neural-network machine-learning

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