Conceitos Básicos
Differentiable programming enables end-to-end differentiation of complex computer programs, allowing for gradient-based optimization of program parameters.
Resumo
Differentiable programming allows for automatic adjustment of program parameters for tasks like image recognition and text generation.
The book covers differentiation, probabilistic learning, differentiable programs, differentiation through programs, and smoothing programs.
It aims to provide a comprehensive introduction to differentiable programming with a focus on core mathematical tools.
The content is structured into parts covering fundamentals, differentiable programs, differentiation techniques, smoothing programs, and optimizing programs.
Estatísticas
Differentiable programming is a programming paradigm where complex computer programs can be differentiated automatically.
Derivative-based optimization is more efficient than derivative-free optimization.
Autodiff revolutionized the manual implementation of gradients in functions for optimization tasks.
Citações
"Autodiff is a game changer because it allows users to focus on quickly and creatively experimenting with functions for their tasks."
"Differentiation is useful beyond deep learning: for instance in reinforcement learning, probabilistic programming and scientific computing in general."