Tag Archives: L1 Regularization
Regularization Techniques to Improve Model Generalization
Introduction In our last discussion, we explored dropout regularization techniques, which involve randomly setting a fraction of the activations to zero during training. This helps prevent overfitting by encouraging the network to learn redundant representations and improving generalization. Today, we will extend our focus to other regularization methods, including L1 and L2 regularization, label smoothing,…
Demystifying Neural Networks: Architectures, Implementations, and Applications
Introduction Overview of Neural Networks Neural networks are computational models inspired by the structure and function of the human brain. They consist of interconnected nodes, or neurons, organized in layers. These neurons process and transmit information, allowing neural networks to learn patterns and relationships within data. Importance and Applications Neural networks have become fundamental to…
Optimizing Machine Learning Models with Effective Regularization Techniques
Introduction Regularization techniques are essential in machine learning to prevent overfitting and improve the generalization of models. These techniques add constraints or penalties to the model to reduce its complexity. In this blog, we will explore various regularization methods, their mathematical definitions, and their effects during the forward and backward passes. L1 and L2 Regularization…