Jacobian-vector product (Jvp) with ForwardDiff.jl in Julia Published 2022-08-18 Download video MP4 360p Recommendations 13:23 What is a Pullback in Zygote.jl? | vector-Jacobian products in Julia 34:46 AMP 54: Daniel Glejzner on Angular Community 1:06:55 Fourier Neural Operators (FNO) in JAX 15:52 Simple reverse-mode Autodiff in Python 02:27 SPH Fluid Simulation in Java and LWJGL 38:51 Physics-Informed Neural Networks in JAX (with Equinox & Optax) 13:14 4 MINUTES AGO: James Webb First Image Before The Big Bang Has FINALLY Been Revealed 26:50 Neural Networks using Lux.jl and Zygote.jl Autodiff in Julia 52:04 Neural Network learns sine function in NumPy/Python with backprop from scratch 06:11 Do we create reality with our mind? A physicist's reply. 10:48 Solving The 1D & 2D Heat Equation Numerically in Python || FDM Simulation - Python Tutorial #4 36:39 The quantum world: Dreams and delusions | Roger Penrose, Sabine Hossenfelder, Michio Kaku, and more! 07:51 JAX.lax.scan tutorial (for autoregressive rollout) 20:24 G. Vlad and Trouble at MIT from Codeforces Round 928 (Div. 4) Tree DP Similar videos 10:20 What is a vector-Jacobian product (vjp) in JAX? 24:00 Fast Forward and Reverse-Mode Differentiation via Enzyme.jl | Many speakers | JuliaCon 2022 10:12 Simple forward-mode AD in Julia using Dual Numbers and Operator Overloading 1:33:38 JAX MD: A Framework for Differentiable Atomistic Physics 20:55 2022 LLVM Dev Mtg: LAGrad: Leveraging the MLIR Ecosystem for Efficient Differentiable Programming 18:31 [LAFI'22] Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic More results