In the world of artificial intelligence and machine learning, a dense model (or a densely connected layer) refers to a type of layer within a neural network. Here’s what makes it unique:
Key Characteristics:
- Connectivity: Every neuron (or node) in a dense layer is connected to every single neuron in the previous layer. This creates a highly interconnected web.
- Purpose: Dense layers are often used towards the end of a neural network for classification or output tasks. They take the processed information from the previous layers and learn complex relationships within the data to arrive at predictions or decisions.
How It Works, an Analogy:
Think of a dense layer like a large group of experts brought together for a decision-making task. Each expert gets the same information (the outputs of the previous layer), they all weigh up the input with their own unique perspective (weights), and contribute their opinion towards a final decision.
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