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 are artificial neural networks able to represent?

Why is it important to have a large amount of data when using artificial neural networks?

Describe the role of optimization techniques in artificial neural networks.

Explain how artificial neurons in a neural network are inspired by biological neurons.

What is the significance of using a nonlinear activation function in a neural network?

What is the purpose of the loss function in training a neural network?

What are the key components needed to build an artificial neural network?

How does the architecture of a neural network affect its performance?

What are some common loss functions used in neural networks?

In what ways can regression be more powerful than classification in neural networks?