Automatic Differentiation

DaTaobao Tech

Aug 9, 2022 · Artificial Intelligence

Differentiable Programming: Theory, Function Fitting, and Practical Implementations

Differentiable programming augments traditional code with automatic differentiation, enabling gradient‑descent optimization of scientific and UI functions; the article surveys its theory, demonstrates fitting a damping curve via logistic and polynomial models in Julia, Swift, and TensorFlow, and discusses trade‑offs between analytical interpretability and neural‑network flexibility.

Automatic DifferentiationJavaScriptTensorFlow

0 likes · 30 min read

Differentiable Programming: Theory, Function Fitting, and Practical Implementations

360 Smart Cloud

Sep 30, 2021 · Artificial Intelligence

Understanding Computational Graphs and Automatic Differentiation for Neural Networks

This article explains how computational graphs can represent arbitrary neural networks, describes forward and reverse propagation, details the implementation of automatic differentiation with Python and NumPy, and demonstrates building and training a multilayer fully‑connected network on the MNIST dataset using custom graph nodes and optimizers.

Automatic DifferentiationDeep LearningPython

0 likes · 29 min read

Understanding Computational Graphs and Automatic Differentiation for Neural Networks

Python Programming Learning Circle

Aug 27, 2021 · Artificial Intelligence

An Introduction to JAX: Features, Installation, and Comparison with TensorFlow and PyTorch

This article introduces Google’s JAX library, covering its origins, core features such as automatic differentiation, JIT compilation, parallel and vectorized mapping, installation steps, code examples, and a comparative overview with TensorFlow and PyTorch for deep‑learning practitioners.

Automatic DifferentiationDeep LearningGPU

0 likes · 11 min read

An Introduction to JAX: Features, Installation, and Comparison with TensorFlow and PyTorch