Neural Networks
Neural networks are computing systems inspired by the biological neural networks in the human brain. They consist of layers of interconnected nodes (neurons) that process information. Neural networks …
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Definition
Neural networks are computing systems inspired by the biological neural networks in the human brain. They consist of layers of interconnected nodes (neurons) that process information. Neural networks learn to perform tasks by adjusting the strength of connections between nodes based on training data. They're the foundation of modern AI — from image recognition to language understanding.
Key Points
- Structure: input layer, hidden layers, output layer
- Learn by adjusting weights through backpropagation
- Types: feedforward, convolutional (CNN), recurrent (RNN), transformer
- Universal approximators — can theoretically learn any function with enough data
Frequently Asked Questions
Neural networks learn through a process called training: (1) Input data passes through the network and produces an output, (2) The output is compared to the correct answer and the error is calculated, (3) Backpropagation adjusts the weights to reduce error, (4) This process repeats millions of times until the network produces accurate results.
For small networks and datasets: yes. A laptop can handle basic neural networks with scikit-learn or small PyTorch models. For deep learning with large datasets, you'll need a GPU — use Google Colab (free) or cloud GPUs. Many Indian developers start with Colab and graduate to paid cloud compute as needed.
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