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Brain-inspired computing systems that power modern AI
Foundation of Deep Learning
Neural networks are computing systems inspired by biological neural networks in animal brains. They consist of interconnected nodes (neurons) organized in layers, learning to perform tasks by considering examples. They're the foundation of deep learning and power most modern AI systems.
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Basic neural units
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Convolutional networks for images
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Recurrent networks for sequences
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Attention-based architecture
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Generative Adversarial Networks
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How networks learn
Neural networks learn through backpropagation—adjusting connection weights based on prediction errors. During training, they process millions of examples, gradually improving their ability to recognize patterns, make predictions, and generate outputs. The "deep" in deep learning refers to networks with many layers, each learning increasingly abstract representations.
Understand the mathematics and architectures powering AI.