autodifferentiation: Revision history

20 August 2023

• curprev 06:1906:19, 20 August 2023‎ WingerBot talk contribs‎ m 3,739 bytes 0‎ split (on comma) author= into 3 entities in {{quote-book}} and join with semicolons; split (on comma) author= into 2 entities in {{quote-book}} and join with semicolons (3) (with cleanup (manually assisted)) undo

22 April 2023

• curprev 18:2818:28, 22 April 2023‎ AutoDooz talk contribs‎ m 3,739 bytes +27‎ →‎English:Noun: converted bare quote to template undo

1 March 2023

• curprev 23:1323:13, 1 March 2023‎ WingerBot talk contribs‎ m 3,712 bytes −15‎ remove redundant Unicode bidi marks undo

26 October 2022

• curprev 23:5823:58, 26 October 2022‎ Kiwima talk contribs‎ 3,727 bytes +3,075‎ →‎Noun undo

10 May 2022

• curprev 20:2720:27, 10 May 2022‎ Fytcha talk contribs‎ 652 bytes −227‎ moved to automatic differentiation undo
• curprev 20:2120:21, 10 May 2022‎ Fytcha talk contribs‎ 879 bytes +40‎ not SOP; AD is a precise technical term that is much more specific than just any automatically performed differentiation undo Tag: Undo
• curprev 20:1220:12, 10 May 2022‎ Equinox talk contribs‎ 839 bytes −40‎ →‎Noun: basically SoP phrases don't require a synonym line undo Tag: Reverted
• curprev 20:0920:09, 10 May 2022‎ Fytcha talk contribs‎ 879 bytes +344‎ #: {{syn|en|automatic differentiation}} # {{lb|en|medicine}} {{rfdef|en}} ====Derived terms==== * {{l|en|autodiff}} ====Translations==== {{trans-top|mathematics, computing}} * Finnish: {{t-needed|fi}} * French: {{t-needed|fr}} {{trans-mid}} * German: {{t|de|automatisches Differenzieren|n}} * Polish: {{t-needed|pl}} {{trans-bottom}} undo

28 April 2022

• curprev 19:0819:08, 28 April 2022‎ Equinox talk contribs‎ 535 bytes +535‎ Created page with "==English== {{wp|Automatic differentiation}} ===Etymology=== {{pre|en|auto|differentiation}} ===Noun=== {{en-noun|-}} # {{lb|en|math|computing}} The automatic differentiation of a function by a computer. #* '''2021''', Joe Papa, ''PyTorch Pocket Reference'' (page 48) #*: The <code>backward()</code> function uses PyTorch's automatic differentiation package, <code>torch.autograd</code>, to differentiate and compute gradients of tensors based on the chain..."