Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Architecture

The video introduces the concepts of neurons, bias, and activation functions in neural networks. It explains how neurons are connected to form a network and the role of hyperparameters in training. The structure of deep neural networks is explored, focusing on fully connected and feedforward networks. The video concludes with a discussion on implementing these concepts and hints at future topics like activation functions and bias terms.

How can multiple layers and neurons be structured in a deep neural network?

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What does it mean for a neural network to perform forward computation?

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