Juan Carlos Olamendyjuancolamendy.hashnode.dev·Apr 23, 2024A Comprehensive Guide to Regularization in Machine LearningHave you ever trained a machine learning model that performed exceptionally on your training data but failed miserably on real-world, unseen data? If so, you've encountered overfitting, a common pitfall in machine learning that regularization techniq...DiscussMachine Learning
K Ahameddatailm.hashnode.dev·Jan 21, 2024Train Right, Predict Bright: Regularization Tricks for Futureproof ModelsIn the world of machine learning, the quest for the perfect model is a constant pursuit. We train complex algorithms on mountains of data, hoping they'll learn intricate patterns and make accurate predictions. But sometimes, our eagerness can backfir...DiscussMachine LearningMachine Learning
Akhil Soniakhilworld.hashnode.dev·Sep 1, 2023Early StoppingIn my blog (Regularization of ML models), I have already discussed different methods of regularization for a linear model. The regularization of a model means setting the parameters of the model in such a way that the cost function which is the perfo...Discuss·10 likesMachine Learning
Kavita Ranakavirana.hashnode.dev·Jun 25, 2023Lasso Regression or L1 RegularizationWhat does L1 mean? In Lasso Regression, we add a penalty term to the loss function during model training. The penalty term is proportional to the sum of the absolute values of the coefficients. We calculate the penalty term using the L1 norm, which i...Discuss·3 likesMachine Learning
Dharshini Sankar Rajdharshinisankarraj.hashnode.dev·Jun 10, 2023Regularization Techniques: Taming Overfitting in Deep Learning Neural NetworksOverfitting is a common challenge in deep learning, where a neural network becomes excessively specialized in the training data and fails to generalize well to unseen data. To combat overfitting, various regularization techniques have been developed....Discuss·1 like·193 readsneural networks
Yusuf Olaniyithe-data-trailblazer.hashnode.dev·Jun 6, 2023Improving the Performance of Machine Learning Models - Part 1Introduction istockphoto.com "The artist who aims at perfection in everything achieves it in nothing" - Eugene Delcroix In pursuit of perfection, the French artist - Eugene Delcroix once said "The artist who aims at perfection in everything achiev...Discuss·13 likes·133 readsregularization
Andrea D’Agostinodag.hashnode.dev·Apr 4, 2023L1 vs L2 Regularization in Machine Learning: Differences, Advantages and How to Apply Them in PythonMachine Learning is a discipline that is experiencing enormous development in the technological and industrial fields. Thanks to its algorithms and modeling techniques, it is possible to build models capable of learning from past data, generalizing a...Discuss·116 readsMachine LearningMachine Learning
Jay Galagalacodes.hashnode.dev·Jan 8, 2023Regularization techniques for reducing varianceRegularization is a technique used in machine learning to prevent overfitting and reduce variance in a model. Overfitting occurs when a model is excessively complex and has learned even the noise in the training data, which can negatively impact the ...Discuss·2 likes·105 readsregularization
Jay Galagalacodes.hashnode.dev·Dec 26, 2022Bias and VarianceUnderstanding the different types of errors deeply goes a long way. In-depth knowledge of why they occur and how they can be identified helps to improve model performance significantly. So what are Bias and Variance? Bias and Variance are two types o...Discuss·103 readsbias variance
Bhagirath Deshani bhagirathkd.hashnode.dev·Nov 11, 2022#4 Machine Learning & Data Science Challenge 4What is L1 Regularization (L1 = lasso)? The main objective of creating a model(training data) is to ensure it properly fits the data and reduces the loss. Sometimes the model that is trained will fit the data but it may fail and give a poor perform...Discuss·63 readsMachine Learning & Data Science Interview Challengesregularization