regularization and weight regularization in machine learning
What do you mean by regularization? Explain.Regularization is a very important technique to prevent overfitting in machine learning problems. From a theoretical point of...
Define some examples of linear algebra in machine learning.
Define some examples of linear algebra in machine learning.Some examples of linear algebra in machine learning are as follows-Linear regressionRegularizationPrincipal component analysis (PCA)Singular-value decomposition...
Support Vector Machine(SVM)
Support Vector MachineSupport vector machines are supervised learning models with associated learning algorithms that analyze data after which they are used for classification. Classification...
Probability distributions |Discrete probability distribution
Probability distributionsWe have explored the idea of probability, we can consider the concept of a probability distribution. In situations where the variable being...
Bayesian classifiers |Naive Bayes classifiers
What are Bayesian classifiers?Bayesian classifiers are statistical classifiers. They can predict class membership probabilities, such as the probability that a given tuple belongs to...
Explain the term training test and validation sets.
Explain the term training test and validation sets.Choosing the right dataset for a given classification task is of crucial importance for the generalization performance....
Batch Normalization | Write down the properties of batch normalization.
Batch NormalizationWhat do you understand by batch normalization? Explain.Batch normalization is a recently popularized method for accelerating deep network training by making data standardization...
Deep Neural Networks | Principal Components of a Neural Network
What do you understand by deep neural networks?Neural networks (NN) are powerful statistical learning models that can solve complex classification and regression problems. A...
Explain the working of backpropagation neural networks with neat architecture and flowchart.
Architecture of backpropagation networkA back propagation neural network is a multilayer, feedforward neural network. It is made up of an input layer, a hidden...
Backpropagation algorithm | Application of Backpropagation
Backpropagation algorithmOne of the most important developments in neural networks is the backpropagation algorithm. The error backpropagation algorithm is used for training multilayer perceptrons...
