PyTorch's eager execution (`tensor.backward()`) felt like NumPy with GPUGraphics processing unit — a chip optimized for parallel math; now used for graphics, ML, and scientific computing. acceleration — researchers debugged models interactively instead of rebuilding static graphs. It overtook TensorFlow in academic papers and powered transformers, diffusion modelA generative AI approach that learns to denoise data — powers many image and video generators.s, and Meta's LlamaMeta's open family of large language models — released weights that sparked an open-source AI wave. training stack.

PyTorch made dynamic neural networkA model of connected layers that learns from examples — the core of modern deep learning.s the default for AI research.

Pythonic APIs and autograd lowered the barrier for deep learningNeural networks with many layers that learn hierarchical representations from data. experimentation.