Read the below passage and answer the questions. Artificial Neural Networks…
2024
Read the below passage and answer the questions.
Artificial Neural Networks (ANNs) are computational models inspired by the human brain's neural networks. They consist of inter-connected nodes, or neurons, organized into layers: an input layer, one or more hidden layers, and an output layer. Each connection between neurons has a weight that adjusts as learning progresses, allowing the network to adapt and improve its performance. ANNs are particularly effective in recognizing patterns, making them valuable for tasks such as image and speech recognition, Natural language processing, and predictive analytics. Learning in ANNs typically involves training algorithms like backpropagation, which minimize the error by adjusting the weights. As a subset of machine learning, ANNs have revolutionized the field of Artificial Intelligence by providing solutions to complex problems that traditional algorithms struggle with.
What is the role of Back Propagation Algorithm?
- A.
To reduce error
- B.
To secure network
- C.
To control speed of data
- D.
To add different layers
Attempted by 59 students.
Show answer & explanation
Correct answer: A
Answer: Back Propagation Algorithm is used to reduce a neural network's prediction error by computing gradients of the loss with respect to the network's weights and updating those weights to minimize the loss.
Key idea: use the chain rule to propagate the error from the output layer back through the hidden layers so each weight can be adjusted in the direction that decreases the loss.
Step 1 — Forward pass: compute the network's outputs for an input and evaluate the loss (error) using a loss function.
Step 2 — Backward pass: compute gradients of the loss with respect to each weight by applying the chain rule layer by layer (this is the "backpropagation" computation).
Step 3 — Weight update: adjust each weight using the computed gradient, typically with gradient descent: new weight = weight − learning_rate × gradient.
Notes: repeated application of these steps over many training examples causes the network to converge toward lower error. Variants like stochastic gradient descent, momentum, Adam, and others change how gradients are used, but the core role of backpropagation — computing gradients to reduce error — remains the same.
A video solution is available for this question — log in and enroll to watch it.