Deep Learning
Deep learning is machine learning with neural networks stacked in many layers, so the system learns the features by itself instead of a human hand-picking them. It is the technique behind today's language, image and speech models.
No jargon, no math
In plain words, what is it?
In classic machine learning a human still does the clever part: choosing which features the model should look at. Deep learning removes that job. You feed the network raw material (pixels, sound waves, words) and each layer learns a slightly more abstract representation: edges become shapes, shapes become parts, parts become a faulty seal. Training is the usual loop (predict, measure the error, adjust) but the numbers being tuned run into the billions, which is why deep learning took off only once GPUs made that cheap. The trade-offs are real: with small datasets a simpler model usually wins, and with billions of weights nobody can point at the reason for a single decision.
Terms that live nearby.
Neural Network
A neural network is a function built from layers of simple units. Each unit multiplies its inputs by weights, adds them, passes the sum through a small nonlinearity, and forwards the result. Training adjusts the weights until the whole stack produces useful outputs.
ReadMachine Learning
Machine learning is a way of building software where the algorithm tunes its own internal numbers from examples, instead of a programmer writing every rule. You supply data and correct answers; the finished product is a model that predicts on inputs it has never seen.
ReadGPU
A GPU is a processor built to run thousands of small calculations at the same time. Designed for graphics, it turned out to be the perfect engine for AI, because running a neural network is mostly one enormous pile of matrix multiplications.
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