Lecture 5 - Automatic Differentiation Implementation Published 2022-09-26 Download video MP4 360p Recommendations 1:26:56 Lecture 6 - Fully connected networks, optimization, initialization 14:25 What is Automatic Differentiation? 44:20 Lecture - 12 GPU Acceleration 59:59 Lecture 7 - Neural Network Abstractions 1:02:48 A Complete .NET Developer's Guide to Span with Stephen Toub 1:19:30 Lecture 9 - Normalization and Regularization 19:33 Automatic Differentiation 46:37 Lecture 15 - Training Large Models 1:27:41 Programming in Modern C with a Sneak Peek into C23 - Dawid Zalewski - ACCU 2023 13:17 Intuition behind reverse mode algorithmic differentiation (AD) 45:22 Lecture 11 - Hardware Acceleration 1:19:42 Lecture 14 - Implementing Convolutions 46:18 10 ML algorithms in 45 minutes | machine learning algorithms for data science | machine learning 56:49 Keynote: Gang of None? Design Patterns in Elixir - José Valim | ElixirConf EU 2024 1:10:16 Lecture 20 - Transformers and Attention 53:07 Stephan Hoyer: "Improving PDE solvers and PDE-constrained optimization with deep learning and di..." 57:55 Lecture 1 - Introduction and Logistics 34:50 Dive Into Deep Learning, Lecture 2: PyTorch Automatic Differentiation (torch.autograd and backward) Similar videos 36:02 Lecture 5 Part 2: Forward Automatic Differentiation via Dual Numbers 1:03:35 Lecture 4 - Automatic Differentiation 22:48 L6.2 Understanding Automatic Differentiation via Computation Graphs 32:46 Lecture 5 Part 3: Differentiation on Computational Graphs 37:38 [OLD] Lecture 2.4: Automatic Differentiation (DLVU) 09:03 L6.3 Automatic Differentiation in PyTorch -- Code Example 46:03 Lecture 5 (FDTD) -- Formulation of 1D FDTD 08:32 DL5.5 - Automatic differentiation: BackProp revisted 1:11:16 Lecture 6: Backpropagation 56:43 From automatic differentiation to message passing 47:58 Differentiable Programming Part 1: Reverse-Mode AD Implementation More results