🧠 How do neural networks work?

Neural networks are like tiny brains inside computers! 🤖 They learn by looking at lots of examples, just like how you learn by seeing things many times. Imagine a robot that learns to recognize cats by looking at pictures of cats and dogs. The more pictures it sees, the better it gets! �

Neural networks have layers of tiny helpers called neurons. Each neuron helps the computer make a small decision. Together, they solve big problems!

Neural networks are computer systems inspired by our brains! 🧠 They have layers of artificial neurons that work together to solve problems. Here’s how they learn:

• Input Layer: Takes in data (like pictures or sounds).
• Hidden Layers: Process the data step by step.
• Output Layer: Gives the final answer (like "This is a cat!").

They learn by adjusting their connections when they make mistakes, just like practicing a game until you get better! 🎮

Neural networks are advanced algorithms modeled after the human brain. They consist of interconnected nodes (neurons) organized in layers:

1. Input Layer: Receives raw data (e.g., pixels of an image).
2. Hidden Layers: Perform computations using weights and activation functions to detect patterns (e.g., edges, shapes).
3. Output Layer: Produces the result (e.g., classifying an image as a cat).

They learn via backpropagation: adjusting weights based on errors to improve accuracy. Training requires massive datasets and computing power. Today, they power AI like voice assistants and self-driving cars! 🚗💡