Deep Learning - Crash Course 2023 - Program Overview

The video tutorial explains the concept of neurons and the parameters West and B, aiming to match labeled inputs to outputs. It introduces gradient descent as a method to optimize these parameters by minimizing the loss function. The tutorial then guides through implementing a Sigma Neuron class in Python, detailing methods for sigmoid and loss computation. It further explains how to compute gradient descent for parameters West and B and concludes with a method to fit the data by updating these parameters.

What is the primary goal when adjusting the parameters West and B in a neuron model?

• To match labeled inputs with labeled outputs

• To minimize the number of inputs

• To maximize the loss function

• To increase the complexity of the model

Which method is used to iteratively solve for the correct parameters in the neuron model?

• Backpropagation

• Stochastic Gradient Descent

• Random Search

• Gradient Descent

In the Sigma neuron class, what is the purpose of the sigmoid method?

• To update the learning rate

• To initialize the parameters

• To compute the loss function

• To return the value of 1 / (1 + e^(West * X + B))

What does the loss function in the Sigma neuron class represent?

• The learning rate

• The product of West and B

• The sum of all inputs

• The difference between predicted and actual outputs

How are the parameters West and B updated during the fitting process?

• By multiplying them with the loss function

• By subtracting the product of the learning rate and their respective gradients

• By dividing them by the number of inputs

• By adding the learning rate to them