Develop an AI system to solve a real-world problem : Building Artificial Neural Networks

This video tutorial explains how to build artificial neural networks, highlighting their ability to perform classification and regression tasks. It discusses the data requirements, structure, and mathematical tools used in neural networks, such as calculus and optimization techniques. The tutorial provides a practical example using the PyTorch library to build, train, and evaluate a neural network on book sales data. It concludes with a brief introduction to unsupervised learning.

What is a key requirement for artificial neural networks to effectively learn complex relationships?

• No data at all

• A single data point

• A large dataset

• A small dataset

What is the role of a bias term in a neuron?

• To divide the weighted sum

• To multiply the weighted sum

• To add a constant to the weighted sum

• To subtract from the weighted sum

Which function is commonly used as an activation function in neural networks?

• Linear function

• Quadratic function

• Exponential function

• Rectified Linear Unit (ReLU)

What is the purpose of a loss function in neural networks?

• To maximize the loss

• To ignore the loss

• To minimize the loss

• To increase the loss

What is the most commonly used loss function for regression tasks?

• Mean Absolute Error

• Mean Squared Error

• Cross-Entropy Loss

• Hinge Loss

Which library is used in the video for implementing neural networks?

• Scikit-learn

• TensorFlow

• Keras

• PyTorch

What is the role of the optimizer in training neural networks?

• To update the parameters for better performance

• To freeze the parameters

• To delete the parameters

• To increase the loss

What does setting the model to evaluation mode do?

• Deletes the parameters

• Increases the parameters

• Allows parameters to change

• Freezes the parameters

How does the neural network's performance in classification compare to regression in the video?

• Classification is more accurate

• Regression is more accurate

• Both are equally accurate

• Neither is accurate

What is a sign of overfitting in neural networks?

• Training accuracy is lower than testing accuracy

• Training and testing accuracy are the same

• Testing accuracy is much higher than training accuracy

• Training accuracy is much higher than testing accuracy