AI Tools Masterclass Interview Questions

The main difference between supervised and unsupervised learning is that supervised learning involves training a model on labeled data, where the correct output is already known, whereas unsupervised learning involves training a model on unlabeled data, where the model must find patterns or relationships in the data on its own.

A neural network learns from data through a process called backpropagation, where the network adjusts its weights and biases to minimize the error between its predictions and the actual outputs, allowing it to learn complex patterns in the data.

The purpose of regularization is to prevent overfitting by adding a penalty term to the loss function, which discourages large weights and encourages simpler models, improving the model's ability to generalize to new data.

Transfer learning is a technique where a pre-trained model is used as a starting point for a new, but related, task, allowing the model to leverage the knowledge it has learned from the previous task and fine-tune it for the new task, reducing the need for large amounts of training data.

A generative model learns to generate new data samples that are similar to the training data, whereas a discriminative model learns to predict the probability of a given input belonging to a particular class, with generative models focusing on the underlying distribution of the data and discriminative models focusing on the decision boundary between classes.