Deep Learning - Deep Neural Network for Beginners Using Python - Gradient Descent Versus Perceptron

The video tutorial covers the perceptron and gradient descent algorithms, highlighting their similarities and differences. The perceptron algorithm focuses on adjusting for misclassified points, while gradient descent considers all points to optimize the solution boundary. The tutorial also introduces the concept of neural networks, explaining how logistic regression forms the basis of a single neuron, and how combining multiple neurons creates a neural network.

What is the primary difference between the perceptron and gradient descent algorithms?

• Perceptron updates weights for every point, while gradient descent updates only for misclassified points.

• Perceptron updates weights only for misclassified points, while gradient descent updates for every point.

• Perceptron uses a fixed learning rate, while gradient descent uses a variable learning rate.

• Perceptron is used for regression tasks, while gradient descent is used for classification tasks.

How does the perceptron algorithm adjust the decision boundary?

• It moves the boundary closer to correctly classified points.

• It moves the boundary away from all points.

• It adjusts the boundary based on misclassified points only.

• It does not adjust the boundary at all.

What is the goal of the gradient descent algorithm when adjusting the decision boundary?

• To minimize the distance between the boundary and all points.

• To maximize the distance between the boundary and correctly classified points.

• To maximize the distance between the boundary and misclassified points.

• To minimize the distance between the boundary and misclassified points.

What is a single neuron in the context of neural networks?

• A logistic regression model

• A support vector machine

• A perceptron algorithm

• A gradient descent algorithm

How is a neural network formed?

• By combining multiple support vector machines

• By combining multiple gradient descent algorithms

• By combining multiple perceptron algorithms

• By combining multiple logistic regression models