What is a neural network (simplified) in AI for Everyone - Practice Questions & Exercises

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the basic structure of a neural network

Imagine a neural network as a group of friends passing notes to solve a problem together. Which part of the neural network acts like the friends passing notes?

A The data input only

B The computer hardware

C The final output only

D The layers of neurons

💡 Hint

Think about who is actively working together inside the network.

The layers of neurons are like friends passing notes, each layer processes information and passes it on to the next.

🔍 Analysis

intermediate

Output of a simple neural network forward pass

What is the output of this simple neural network forward pass code?

AI for Everyone

import numpy as np

def simple_forward(x, w, b):
    return np.dot(x, w) + b

x = np.array([1, 2])
w = np.array([0.5, -1])
b = 0.1
output = simple_forward(x, w, b)
print(output)

A 1.1

B 0.1

C -1.4

D -0.9

💡 Hint

Calculate dot product: (1*0.5) + (2*-1) + 0.1

The dot product is 0.5 + (-2) = -1.5, then add bias 0.1, total -1.4. Check carefully.

❓ Model Choice

advanced

Choosing the right neural network type for image recognition

You want to build a model to recognize objects in photos. Which neural network type is best suited for this task?

A Convolutional Neural Network (CNN)

B Recurrent Neural Network (RNN)

C Feedforward Neural Network without convolution

D Autoencoder

💡 Hint

Think about which network type is designed to handle images and spatial data.

CNNs are designed to process images by detecting patterns like edges and shapes using convolution layers.

❓ Hyperparameter

advanced

Effect of learning rate on training a neural network

What happens if the learning rate is set too high when training a neural network?

A The model may fail to learn and the loss might bounce around or increase

B The model will learn faster and reach perfect accuracy immediately

C The model will ignore the training data

D The model will always underfit the data

💡 Hint

Think about how big steps in learning affect stability.

A too high learning rate causes the model to take big steps, missing the best solution and causing unstable training.

❓ Metrics

expert

Interpreting confusion matrix metrics for a neural network classifier

A neural network classifier outputs the following confusion matrix:

True Positive (TP): 40
False Positive (FP): 10
True Negative (TN): 30
False Negative (FN): 20

What is the precision of this model?

A 0.66

B 0.80

C 0.67

D 0.75

💡 Hint

Precision = TP / (TP + FP)

Precision measures how many predicted positives are actually correct: 40 / (40 + 10) = 0.8