A neural network is a computing system inspired by biological neurons. It consists of interconnected layers of nodes that process information and learn patterns from data through training.

How do neural networks learn?

Neural networks learn through backpropagation and gradient descent. They adjust connection weights to minimize prediction errors on training data, gradually improving their accuracy.

What types of neural networks exist?

Common types include feedforward networks, CNNs for images, RNNs/LSTMs for sequences, transformers for language, and GANs for generation. Each is optimized for different tasks.

Can I visualize how neural networks work?

Yes, tools like TensorBoard, Netron, and interactive websites like TensorFlow Playground help visualize network architectures, training progress, and learned features.

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