Exploring Clustering and Deep Learning

What is clustering in machine learning?

• Clustering is a technique in machine learning that groups similar data points together.

• Clustering is a method for predicting future data points.

• Clustering is a process of removing outliers from a dataset.

• Clustering is a technique that sorts data points in ascending order.

Name two common clustering techniques.

• Decision trees

• Linear regression

• Support vector machines

• K-means clustering, Hierarchical clustering

How does K-means clustering work?

• K-means clustering groups data based on the distance to the nearest data point only.

• K-means clustering uses a fixed number of clusters determined by the user without any iterations.

• K-means clustering is a supervised learning algorithm that requires labeled data.

• K-means clustering is an iterative algorithm that partitions data into K clusters by minimizing the variance within each cluster.

Provide an example of a supervised learning algorithm.

• Support Vector Machine

• K-Means Clustering

• Linear Regression

• Decision Tree

What is a neural network?

• A neural network is a software application used for video editing.

• A neural network is a computational model made up of interconnected nodes that process information similarly to the human brain.

• A neural network is a physical network of neurons in the human body.

• A neural network is a type of computer virus that spreads through networks.

Describe the basic architecture of a neural network.

• A neural network operates solely on binary data without any weights.

• A neural network is composed of an input layer, hidden layers, and an output layer, with nodes connected by weighted edges.

• A neural network is made up of only input and output layers without hidden layers.

• A neural network consists of a single layer with no connections.

What role do activation functions play in neural networks?

• Activation functions only work in convolutional layers.

• Activation functions are used to increase the speed of training.

• Activation functions are responsible for data normalization.

• Activation functions enable neural networks to learn non-linear relationships.

What is overfitting in the context of machine learning?

• Overfitting happens when a model is trained on too little data, leading to generalization issues.

• Overfitting is when a model performs poorly on both training and new data due to lack of data.

• Overfitting is when a model performs well on training data but poorly on new data due to excessive complexity.

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns.

What are precision and recall in model evaluation?

• Precision is the ratio of false positives to true negatives; Recall is the ratio of false negatives to true positives.

• Precision is the ratio of true positives to the total number of predictions; Recall is the ratio of true negatives to the total number of actual positives.

• Precision is the ratio of true positives to the sum of true and false positives; Recall is the ratio of true positives to the sum of true positives and false negatives.

• Precision measures the total number of correct predictions; Recall measures the total number of incorrect predictions.

Explain the concept of loss function in deep learning.

• The loss function is a type of activation function in neural networks.

• In deep learning, a loss function measures the discrepancy between predicted outputs and actual targets, guiding model training by minimizing this loss.

• A loss function is used to increase the accuracy of the model.

• Loss functions are only relevant in supervised learning.