Simple forward-mode AD in Julia using Dual Numbers and Operator Overloading Published 2022-11-15 Download video MP4 360p Recommendations 20:15 Adjoint Sensitivities in Julia with Zygote & ChainRules 1:06:55 Fourier Neural Operators (FNO) in JAX 10:35 A programming language to heal the planet together: Julia | Alan Edelman | TEDxMIT 40:32 Algorithmic Differentiation 1 09:44 Jacobian-vector product (Jvp) with ForwardDiff.jl in Julia 1:31:01 Let's pretrain a 3B LLM from scratch: on 16+ H100 GPUs, no detail skipped. 19:06 Factorials, Harmonic Numbers, and Trig 21:53 1 Billion is Tiny in an Alternate Universe: Introduction to p-adic Numbers 19:14 The strange cousin of the complex numbers -- the dual numbers. 25:33 [08x06] Calculus using Julia Automatic Differentiation | ForwardDiff.jl, ReverseDiff.jl and Pluto 50:22 How does an OS boot? //Source Dive// 001 13:50 Duality: Lagrangian and dual problem 15:52 Simple reverse-mode Autodiff in Python 26:13 A Brief Introduction to Julia 43:36 Physics-Informed Neural Networks in Julia 19:26 How Strings Work in C++ (and how to use them) 11:24 Automatic Differentiation in 10 minutes with Julia 19:31 [07x12] Intro to Stochastic Differential Equations in Julia using DifferentialEquations.jl and Pluto Similar videos 28:08 Introduction to Julia: Automatic differentiation with dual numbers 14:25 What is Automatic Differentiation? 25:26 1.4. Automatic Derivation with Dual Numbers 09:11 Automatic Differentiation in Julia with ForwardDiff.jl 09:15 The Dual Numbers 47:58 Differentiable Programming Part 1: Reverse-Mode AD Implementation 47:39 6.1 Optimization Method - Automatic Differentiation 3:54:05 Learn Julia in 4 hours in 4K | Full Course | Julia for Absolute Beginners 59:00 Differentiable Programming in C++ - Vassil Vassilev & William Moses - CppCon 2021 15:01 Automatic differentiation in Ruby More results