What is the difference between a discriminative and a generative model?

In simple words, a discriminative model makes predictions on the unseen data based on conditional probability and can be used either for classification or regression problem statements. On the contrary, a generative model focuses on the distribution of a dataset to return a probability for a given example.

What is generative learning?

Abstract. Generative Learning Theory (GLT) suggests that learning occurs when learners are both physically and cognitively active in organizing and integrating new information into their existing knowledge structures.

What is discriminative learning?

Discriminative learning refers to any classification learning process that classifies by using a model or estimate of the probability P(y\,\vert x) without reference to an explicit estimate of any of P(x), P(y, x), or P(x \vert \,y), where y is a class and x is a description of an object to be classified.

What is the loss function that is used when solving Multiclassclassification problem?

Abstract—Cross-entropy is the de-facto loss function in modern classification tasks that involve distinguishing hundreds or even thousands of classes.

What is generative machine learning?

Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset.

What are the biggest challenges facing machine learning today?

The number one problem facing Machine Learning is the lack of good data. While enhancing algorithms often consumes most of the time of developers in AI, data quality is essential for the algorithms to function as intended.

What are the enemies of machine learning?

Noisy data, dirty data, and incomplete data are the quintessential enemies of ideal Machine Learning. The solution to this conundrum is to take the time to evaluate and scope data with meticulous data governance, data integration, and data exploration until you get clear data.

What are the most common use cases for machine learning?

Contrary to what one might expect, Machine Learning use cases are not that difficult to come across. The most common examples of problems solved by machine learning are image tagging by Facebook and spam detection by email providers.

Why is it so hard to buy machine learning solutions?

By now, policymakers are used to hearing claims like this in sales pitches, and they should appropriately raise some skepticism. One reason it’s hard to be a good buyer of machine learning solutions is that there are so many overstated claims. It’s not that people are intentionally misstating the results from their algorithms.