What Are Deep Neural Networks ?
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Deep Neural Networks (DNNs): Key components of Artificial Intelligence (AI), resembling a supercharged, layered brain structure.

Structure of DNNs:

Input Layer: Where data (like images or sounds) is fed into the network.
Hidden Layers: The core of the network where learning occurs through multiple layers.
Output Layer: Provides the final decision or identification by the network.
Learning Process:

Based on connections and weights between neurons in different layers.
Weights adjust as the network learns from errors (e.g., misidentifying an object).
Automatically learns to identify features, starting from simple to complex.
Challenges and Requirements:

Need for substantial data and computing power (GPUs often used for training).
Risk of bias if the training data lacks diversity.
Real-World Applications:

Found in voice recognition, recommendation systems, medical diagnoses, and more.
General Impact:

DNNs have significantly advanced the capabilities and applications of AI.
This summary encapsulates the essence of DNNs in a more structured, bullet-point format.

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References
https://en.wiktionary.org/wiki/what
https://en.wiktionary.org/wiki/are
https://en.wikipedia.org/wiki/Deep_learning#Deep_neural_networks
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What Are Deep Neural Networks?