Loss Function – Ghazi Hudeihed

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Blog, Machine Learning

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,…

July 9, 2024 Ghazi Hudeihed

active learning CNNs CutMix Data Augmentation Deep Learning Dropout feature selection generalization image classification L1 Regularization L2 Regularization Label Smoothing Loss Function Machine Learning Mean Squared Error medical diagnostics MixUp model confidence model uncertainty Monte Carlo dropout Neural Networks optimization.Overfitting Regularization segmentation masks Stochastic Gradient Descent Weight Decay weight updates

Blog, Machine Learning

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…

May 31, 2024 Ghazi Hudeihed

Artificial Intelligence Batch Normalization CutMix Data Augmentation Deep Learning DropConnect Dropout Early Stopping Elastic Net Gradient Descent L1 Regularization L2 Regularization Label Smoothing Lasso Layer Normalization Loss Function Machine Learning Max Norm Max Norm Regularization MixUp ML Optimization Model Generalization Neural Networks Overfitting Regularization Ridge Stochastic Depth Weight Decay