Neural networks are computational models inspired by the human brain's structure and function. They consist of layers of interconnected nodes (neurons) that process data through weighted connections. Here's a breakdown:
🧠 Core Components
- Input Layer: Receives raw data (e.g., images, text)
- Hidden Layer(s): Processes data through non-linear transformations
- Output Layer: Produces final predictions or classifications
- Weights & Biases: Adjustable parameters that determine the model's learning
🔁 Training Process
- Forward propagation: Data moves through the network
- Loss calculation: Measures prediction accuracy
- Backpropagation: Adjusts weights via gradient descent
- Iterative refinement: Repeats until optimal performance
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For deeper exploration, check our Machine Learning Basics tutorial to understand foundational concepts before diving into neural networks. Applications span from computer vision to natural language processing — learn more about AI applications to discover how neural networks power modern technology.