| mstksg/backprop |
180 |
|
18 |
0 |
over 2 years ago |
23 |
July 23, 2023 |
4 |
bsd-3-clause |
Haskell |
| Heterogeneous automatic differentiation ("backpropagation") in Haskell |
| differential-machine-learning/notebooks |
101 |
|
0 |
0 |
over 3 years ago |
0 |
|
2 |
|
Jupyter Notebook |
| Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers. |
| kul-optec/AbstractOperators.jl |
28 |
|
0 |
0 |
almost 3 years ago |
0 |
|
2 |
other |
Julia |
| Abstract operators for large scale optimization in Julia |
| o1lo01ol1o/diffhask |
26 |
|
0 |
0 |
almost 7 years ago |
0 |
|
7 |
mit |
Haskell |
| DSL for forward and reverse mode automatic differentiation in Haskell. Port of DiffSharp. |
| differential-machine-learning/appendices |
25 |
|
0 |
0 |
over 3 years ago |
0 |
|
0 |
|
|
| Complement the article 'Differential Machine Learning' (Huge & Savine, 2020), including mathematical proofs and important implementation details for production |
| SchusterLab/qoc |
11 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
mit |
Python |
| GRAPE with autograd on the TDSE and LME in Python |
| harveyslash/sympyle |
9 |
|
0 |
0 |
over 7 years ago |
2 |
July 12, 2018 |
0 |
gpl-3.0 |
Python |
| Automatic differentiation in python |
