Automatic Differentiation

[Adelta: Automatic Differentiation for Discontinuous Programs — Part 1: Math Basics] [Adelta: Automatic Differentiation for Discontinuous Programs — Part 2: Introduction to the DSL] [Adelta Tutorial — Part 1: Differentiating a Simple Shader Program] [Adelta Tutorial — Part 2: Raymarching Primitive] [Adelta Tutorial — Part 4: Animating the Celtic Knot] …

The Swift programming language is a key tool in the construction of PassiveLogic’s digital twins and autonomous control systems. We chose it in large part because it has a capability unique among systems languages: language-integrated automatic differentiation. …

[Adelta: Automatic Differentiation for Discontinuous Programs — Part 1: Math Basics] [Adelta: Automatic Differentiation for Discontinuous Programs — Part 2: Introduction to the DSL] [Adelta Tutorial — Part 1: Differentiating a Simple Shader Program] [Adelta Tutorial — Part 3: Animating the SIGGRAPH logo] [Adelta Tutorial — Part 4: Animating the…

[Adelta: Automatic Differentiation for Discontinuous Programs — Part 1: Math Basics] [Adelta: Automatic Differentiation for Discontinuous Programs — Part 2: Introduction to the DSL] [Adelta Tutorial — Part 2: Raymarching Primitive] [Adelta Tutorial — Part 3: Animating the SIGGRAPH logo] [Adelta Tutorial — Part 4: Animating the Celtic Knot] In…

[Adelta: Automatic Differentiation for Discontinuous Programs — Part 1: Math Basics] [Adelta Tutorial — Part 1: Differentiating a Simple Shader Program] [Adelta Tutorial — Part 2: Raymarching Primitive] [Adelta Tutorial — Part 3: Animating the SIGGRAPH logo] [Adelta Tutorial — Part 4: Animating the Celtic Knot] In the previous post…

[Adelta: Automatic Differentiation for Discontinuous Programs — Part 2: Introduction to the DSL] [Adelta Tutorial — Part 1: Differentiating a Simple Shader Program] [Adelta Tutorial — Part 2: Raymarching Primitive] [Adelta Tutorial — Part 3: Animating the SIGGRAPH logo] [Adelta Tutorial — Part 4: Animating the Celtic Knot] Over the…

Introduction: there are four methods for the automatic differentiation: •Manual •Numerical •Symbolic Automatic Manual differentiation: It is to look at the function and manually differentiate it using basic derivative rules, code up the result and call it Numerical (Finite differences): Which use the method of finite differences to approximate derivatives…

Small examples and more — This post examines some backward() function examples about the autograd (Automatic Differentiation) package of PyTorch. As you already know, if you want to compute all the derivatives of a tensor, you can call backward() on it. The torch.tensor.backward function relies on the autograd function torch.autograd.backward …

There is a link from the chain rule to automatic differentiation. Let us cross the bridge from mathematics to computer science. — Neural networks are cool. The different machine learning frameworks are even cooler. As you may know, modern neural networks are large formulas with a huge number of variables. Given a problem, these frameworks will help you find a suitable set of parameter values, with a process called “training”. This training…