Key Insights Deep-learning algorithms exhibit remarkable performance in practice, but it is not well-understood why they are able to generalize despite having more parameters than training examples.

There are dozens of machine learning algorithms, ranging in complexity from linear regression and logistic regression to deep neural networks and ensembles (combinations of other models).

Linear regression in machine learning is considered as the basis or foundation in machine learning. This video explains the entire maths and the algorithm behind What is Linear Regression in ...

Machine learning algorithms for predicting or categorizing data include classification and regression techniques. Regression algorithms are used to forecast a continuous numerical value, such as the ...

The primary goal of a linear regression training algorithm is to compute coefficients that make the difference between reality and the model’s predictions consistently small.

This regression algorithm has several applications across the industry for product pricing, real estate pricing, marketing departments to find out the impact of campaigns. Unlike linear regression ...

The phenomenon of benign overfitting is one of the key mysteries uncovered by deep learning methodology: deep neural networks seem to predict well, even with a perfect fit to noisy training data.

Machine Learning Algorithm Development Over time, there have been notable developments in the field of machine learning as algorithms have become increasingly complex and task-specific. Data ...