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.

In what ways can the arrangement of neurons in a layered architecture impact the model's flexibility?

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What challenges are associated with training deep neural networks?

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