Transform how you grasp deep neural networks today

Key Components of Deep Neural Networks

DNNs consist of multiple layers, each performing specific functions to process and analyze data. These layers include:

• Input Layer: Receives raw data for processing.
• Hidden Layers: Perform complex computations to identify patterns.
• Output Layer: Provides the final prediction or decision.

Each layer's neurons are interconnected, allowing the network to learn from data iteratively. Training these networks involves adjusting weights and biases through techniques like backpropagation, optimizing the model's performance.