Deep Learning

Deep Learning is a subset of machine learning that trains multi-layer neural networks—often with millions or billions of parameters—to learn hierarchical patterns directly from raw data such as images, audio, or text. A model’s input layer ingests features; successive hidden layers apply linear transformations and nonlinear activations; the output layer predicts classes, words, or continuous values. Back-propagation and stochastic gradient descent adjust weights to minimize loss, while regularizers, batch normalization, and dropout prevent overfitting. Landmark architectures include CNNs for vision, RNNs and Transformers for language, and diffusion models for generative art. Training leverages GPUs, TPUs, or specialized AI accelerators, and frameworks like PyTorch and TensorFlow provide automatic differentiation and distributed-data-parallel tooling. Key metrics—accuracy, perplexity, mean-average-precision—measure performance; challenges include data hunger, explainability, and energy cost. By surpassing human benchmarks in vision, speech, and protein folding, Deep Learning underpins today’s LLMs, self-driving cars, and edge-AI devices.