Data Science and Machine Learning (Theory and Projects) A to Z - Deep Neural Networks and Deep Learning Basics: Why Dept

The video discusses the Universal Approximation Theorem, which states that a neural network with a single hidden layer can approximate any function under certain conditions. However, using a single layer may require an impractical number of neurons. The video explains that adding depth to neural networks can reduce the number of neurons and weights needed, without losing representation power. It also highlights the challenges of training deep networks and the importance of tuning hyperparameters. The video concludes by emphasizing the benefits of layered architectures in neural networks.

What does the Universal Approximation Theorem suggest about neural networks?

• They can only model linear functions.

• They can model almost any function with a single hidden layer.

• They require multiple layers to model any function.

• They are only effective with a large number of layers.

Why might depth be preferred over a single layer with many neurons?

• Depth increases the number of neurons required.

• Depth decreases the representation power of the network.

• Depth reduces the number of neurons and weights needed.

• Depth makes the network slower to train.

How does a layered architecture benefit neural networks?

• It increases the computational complexity.

• It limits the types of functions that can be modeled.

• It reduces the total number of neurons and weights.

• It requires more data to train effectively.

What is a key challenge when training deep neural networks?

• They are always faster to train than shallow networks.

• They have no computational challenges.

• They require fewer hyperparameters.

• They are easy to overfit.

Why is depth important in neural networks despite the Universal Approximation Theorem?

• It allows for more complex functions to be modeled.

• It reduces computational complexity while maintaining power.

• It increases the number of neurons required.

• It simplifies the training process.