Differentiable Programming

Differentiable programming is a paradigm that allows users to define and optimize arbitrary programs by taking gradients. It combines traditional programming with automatic differentiation, which is a technique for efficiently computing gradients of functions. Differentiable programming has become increasingly popular in machine learning and deep learning, where it is used to optimize complex models with millions of parameters. By using differentiable programming, users can easily define custom loss functions, constraints, and regularization terms, and optimize them using standard gradient-based methods such as stochastic gradient descent. Differentiable programming also enables end-to-end optimization of entire pipelines, including data preprocessing, feature extraction, and model training. This paradigm has the potential to revolutionize the way we write and optimize programs, making it easier to develop complex models and algorithms that can learn from data.
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