diff --git a/doc/Projects/2021/Project3/html/._Project3-bs000.html b/doc/Projects/2021/Project3/html/._Project3-bs000.html
index dccf24e20..58dd485eb 100644
--- a/doc/Projects/2021/Project3/html/._Project3-bs000.html
+++ b/doc/Projects/2021/Project3/html/._Project3-bs000.html
@@ -160,7 +160,7 @@ MathJax.Hub.Config({
-Nov 14, 2021
+Nov 15, 2021
@@ -192,7 +192,7 @@ MathJax.Hub.Config({
We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.
-This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides from week 43 and/or the textbook by Yadav et al.
+This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides from week 42 and/or the textbook by Yadav et al.
Note: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise.
The basic structure of your project
@@ -222,7 +222,7 @@ equation in one dimension using a standard explicit scheme and neural
networks to solve the same equations.
-For the explicit scheme, you can study for example chapter 10 of the lecture notes in Computational Physics or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by Kristine Baluka Hein at this course are highly recommended.
+For the explicit scheme, you can study for example chapter 10 of the lecture notes in Computational Physics or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by Kristine Baluka Hein and included in the lectures of week 42 at this course are highly recommended.
For the machine learning part you can use your own code from project 2 or the functionality of for example Tensorflow/Keras..
Part a), setting up the problem
diff --git a/doc/Projects/2021/Project3/html/Project3-bs.html b/doc/Projects/2021/Project3/html/Project3-bs.html
index dccf24e20..58dd485eb 100644
--- a/doc/Projects/2021/Project3/html/Project3-bs.html
+++ b/doc/Projects/2021/Project3/html/Project3-bs.html
@@ -160,7 +160,7 @@ MathJax.Hub.Config({
-Nov 14, 2021
+Nov 15, 2021
@@ -192,7 +192,7 @@ MathJax.Hub.Config({
We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.
-This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides from week 43 and/or the textbook by Yadav et al.
+This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides from week 42 and/or the textbook by Yadav et al.
Note: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise.
The basic structure of your project
@@ -222,7 +222,7 @@ equation in one dimension using a standard explicit scheme and neural
networks to solve the same equations.
-For the explicit scheme, you can study for example chapter 10 of the lecture notes in Computational Physics or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by Kristine Baluka Hein at this course are highly recommended.
+For the explicit scheme, you can study for example chapter 10 of the lecture notes in Computational Physics or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by Kristine Baluka Hein and included in the lectures of week 42 at this course are highly recommended.
For the machine learning part you can use your own code from project 2 or the functionality of for example Tensorflow/Keras..
Part a), setting up the problem
diff --git a/doc/Projects/2021/Project3/html/Project3.html b/doc/Projects/2021/Project3/html/Project3.html
index 5fe0f289b..7f581ab7b 100644
--- a/doc/Projects/2021/Project3/html/Project3.html
+++ b/doc/Projects/2021/Project3/html/Project3.html
@@ -194,7 +194,7 @@ MathJax.Hub.Config({
-Nov 14, 2021
+Nov 15, 2021
Paths for project 3
@@ -223,7 +223,7 @@ MathJax.Hub.Config({
We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.
-This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides from week 43 and/or the textbook by Yadav et al.
+This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides from week 42 and/or the textbook by Yadav et al.
Note: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise.
The basic structure of your project
@@ -253,7 +253,7 @@ equation in one dimension using a standard explicit scheme and neural
networks to solve the same equations.
-For the explicit scheme, you can study for example chapter 10 of the lecture notes in Computational Physics or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by Kristine Baluka Hein at this course are highly recommended.
+For the explicit scheme, you can study for example chapter 10 of the lecture notes in Computational Physics or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by Kristine Baluka Hein and included in the lectures of week 42 at this course are highly recommended.
For the machine learning part you can use your own code from project 2 or the functionality of for example Tensorflow/Keras..
Part a), setting up the problem
diff --git a/doc/Projects/2021/Project3/ipynb/Project3.ipynb b/doc/Projects/2021/Project3/ipynb/Project3.ipynb
index 3fb613d09..e29c83394 100644
--- a/doc/Projects/2021/Project3/ipynb/Project3.ipynb
+++ b/doc/Projects/2021/Project3/ipynb/Project3.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "e6c08da8",
+ "id": "2a5e5636",
"metadata": {
"editable": true
},
@@ -14,7 +14,7 @@
},
{
"cell_type": "markdown",
- "id": "0309e266",
+ "id": "fcf25e5d",
"metadata": {
"editable": true
},
@@ -22,14 +22,14 @@
"# Project 3 on Machine Learning, deadline December 17, 2021\n",
"**[Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html)**, Department of Physics, University of Oslo, Norway\n",
"\n",
- "Date: **Nov 14, 2021**\n",
+ "Date: **Nov 15, 2021**\n",
"\n",
"Copyright 1999-2021, [Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html). Released under CC Attribution-NonCommercial 4.0 license"
]
},
{
"cell_type": "markdown",
- "id": "cedebbe4",
+ "id": "6ce728c3",
"metadata": {
"editable": true
},
@@ -39,7 +39,7 @@
},
{
"cell_type": "markdown",
- "id": "9038e49a",
+ "id": "72bbcc49",
"metadata": {
"editable": true
},
@@ -72,14 +72,14 @@
"\n",
"We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.\n",
"\n",
- "This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides [from week 43](https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html) and/or the textbook by [Yadav et al](https://www.springer.com/gp/book/9789401798150).\n",
+ "This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides [from week 42](https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html) and/or the textbook by [Yadav et al](https://www.springer.com/gp/book/9789401798150).\n",
"\n",
"**Note**: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise."
]
},
{
"cell_type": "markdown",
- "id": "89190c80",
+ "id": "b830757c",
"metadata": {
"editable": true
},
@@ -91,7 +91,7 @@
},
{
"cell_type": "markdown",
- "id": "b2944795",
+ "id": "d8414014",
"metadata": {
"editable": true
},
@@ -103,7 +103,7 @@
},
{
"cell_type": "markdown",
- "id": "5d7f5f66",
+ "id": "b34cd53b",
"metadata": {
"editable": true
},
@@ -115,7 +115,7 @@
},
{
"cell_type": "markdown",
- "id": "55083a27",
+ "id": "f1a8c11e",
"metadata": {
"editable": true
},
@@ -127,7 +127,7 @@
},
{
"cell_type": "markdown",
- "id": "26bb3996",
+ "id": "202c4edd",
"metadata": {
"editable": true
},
@@ -139,7 +139,7 @@
},
{
"cell_type": "markdown",
- "id": "99cbbdf4",
+ "id": "c45d9ea8",
"metadata": {
"editable": true
},
@@ -151,7 +151,7 @@
},
{
"cell_type": "markdown",
- "id": "1cf8305f",
+ "id": "0dbde4e8",
"metadata": {
"editable": true
},
@@ -164,14 +164,14 @@
"equation in one dimension using a standard explicit scheme and neural\n",
"networks to solve the same equations.\n",
"\n",
- "For the explicit scheme, you can study for example chapter 10 of the lecture notes in [Computational Physics](https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf) or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by [Kristine Baluka Hein](https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-bs.html) at this course are highly recommended.\n",
+ "For the explicit scheme, you can study for example chapter 10 of the lecture notes in [Computational Physics](https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf) or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by [Kristine Baluka Hein and included in the lectures of week 42](https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html) at this course are highly recommended.\n",
"\n",
"For the machine learning part you can use your own code from project 2 or the functionality of for example **Tensorflow/Keras**.."
]
},
{
"cell_type": "markdown",
- "id": "c76618e8",
+ "id": "09596115",
"metadata": {
"editable": true
},
@@ -185,7 +185,7 @@
},
{
"cell_type": "markdown",
- "id": "ac2c5732",
+ "id": "cda687af",
"metadata": {
"editable": true
},
@@ -197,7 +197,7 @@
},
{
"cell_type": "markdown",
- "id": "ead9d520",
+ "id": "a55bb07a",
"metadata": {
"editable": true
},
@@ -207,7 +207,7 @@
},
{
"cell_type": "markdown",
- "id": "8db95bf3",
+ "id": "f7ee21a8",
"metadata": {
"editable": true
},
@@ -219,7 +219,7 @@
},
{
"cell_type": "markdown",
- "id": "2f93712e",
+ "id": "27ca8f1d",
"metadata": {
"editable": true
},
@@ -229,7 +229,7 @@
},
{
"cell_type": "markdown",
- "id": "62a0980a",
+ "id": "7442e420",
"metadata": {
"editable": true
},
@@ -241,7 +241,7 @@
},
{
"cell_type": "markdown",
- "id": "28f54a78",
+ "id": "ed9561d4",
"metadata": {
"editable": true
},
@@ -252,7 +252,7 @@
},
{
"cell_type": "markdown",
- "id": "e0068e8b",
+ "id": "2df03aa0",
"metadata": {
"editable": true
},
@@ -264,7 +264,7 @@
},
{
"cell_type": "markdown",
- "id": "f9ae2916",
+ "id": "2cdd9796",
"metadata": {
"editable": true
},
@@ -274,7 +274,7 @@
},
{
"cell_type": "markdown",
- "id": "88e71606",
+ "id": "875b6ccc",
"metadata": {
"editable": true
},
@@ -286,7 +286,7 @@
},
{
"cell_type": "markdown",
- "id": "69bc12e7",
+ "id": "b1addadd",
"metadata": {
"editable": true
},
@@ -299,7 +299,7 @@
},
{
"cell_type": "markdown",
- "id": "fa2123d5",
+ "id": "ece46eb6",
"metadata": {
"editable": true
},
@@ -311,7 +311,7 @@
},
{
"cell_type": "markdown",
- "id": "27474b17",
+ "id": "0a88cb12",
"metadata": {
"editable": true
},
@@ -321,7 +321,7 @@
},
{
"cell_type": "markdown",
- "id": "1c4da408",
+ "id": "be9bea11",
"metadata": {
"editable": true
},
@@ -333,7 +333,7 @@
},
{
"cell_type": "markdown",
- "id": "06021617",
+ "id": "107cf59e",
"metadata": {
"editable": true
},
@@ -343,7 +343,7 @@
},
{
"cell_type": "markdown",
- "id": "b845d906",
+ "id": "1e484fd7",
"metadata": {
"editable": true
},
@@ -355,7 +355,7 @@
},
{
"cell_type": "markdown",
- "id": "6c3956bc",
+ "id": "b4cba98b",
"metadata": {
"editable": true
},
@@ -366,7 +366,7 @@
},
{
"cell_type": "markdown",
- "id": "15d51aa1",
+ "id": "9e4e7daf",
"metadata": {
"editable": true
},
@@ -382,7 +382,7 @@
},
{
"cell_type": "markdown",
- "id": "356b0ce1",
+ "id": "8b2745e6",
"metadata": {
"editable": true
},
@@ -399,7 +399,7 @@
},
{
"cell_type": "markdown",
- "id": "5254f0ec",
+ "id": "a2f4a6b2",
"metadata": {
"editable": true
},
@@ -415,7 +415,7 @@
},
{
"cell_type": "markdown",
- "id": "fb586020",
+ "id": "fa4833fa",
"metadata": {
"editable": true
},
@@ -427,7 +427,7 @@
},
{
"cell_type": "markdown",
- "id": "8f43137a",
+ "id": "1686bf75",
"metadata": {
"editable": true
},
@@ -462,7 +462,7 @@
},
{
"cell_type": "markdown",
- "id": "5efcf1de",
+ "id": "63a5058f",
"metadata": {
"editable": true
},
@@ -493,7 +493,7 @@
},
{
"cell_type": "markdown",
- "id": "e53cd89a",
+ "id": "4dd30c40",
"metadata": {
"editable": true
},
@@ -515,7 +515,7 @@
},
{
"cell_type": "markdown",
- "id": "f1b49648",
+ "id": "638d1a96",
"metadata": {
"editable": true
},
diff --git a/doc/Projects/2021/Project3/ipynb/ipynb-Project3-src.tar.gz b/doc/Projects/2021/Project3/ipynb/ipynb-Project3-src.tar.gz
index c9332ae71..38540a20d 100644
Binary files a/doc/Projects/2021/Project3/ipynb/ipynb-Project3-src.tar.gz and b/doc/Projects/2021/Project3/ipynb/ipynb-Project3-src.tar.gz differ
diff --git a/doc/Projects/2021/Project3/pdf/Project3.p.tex b/doc/Projects/2021/Project3/pdf/Project3.p.tex
index 03a195fde..3e8e6205a 100644
--- a/doc/Projects/2021/Project3/pdf/Project3.p.tex
+++ b/doc/Projects/2021/Project3/pdf/Project3.p.tex
@@ -149,7 +149,7 @@ Project 3 on Machine Learning, deadline December 17, 2021
% --- begin date ---
\begin{center}
-Nov 14, 2021
+Nov 15, 2021
\end{center}
% --- end date ---
@@ -195,7 +195,7 @@ All in all, the report should follow the same pattern as the two previous ones,
We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.
-This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides \href{{https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html}}{from week 43} and/or the textbook by \href{{https://www.springer.com/gp/book/9789401798150}}{Yadav et al}.
+This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides \href{{https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html}}{from week 42} and/or the textbook by \href{{https://www.springer.com/gp/book/9789401798150}}{Yadav et al}.
\textbf{Note}: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise.
@@ -226,7 +226,7 @@ finite difference schemes. We will study the solution of the diffusion
equation in one dimension using a standard explicit scheme and neural
networks to solve the same equations.
-For the explicit scheme, you can study for example chapter 10 of the lecture notes in \href{{https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf}}{Computational Physics} or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by \href{{https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-bs.html}}{Kristine Baluka Hein} at this course are highly recommended.
+For the explicit scheme, you can study for example chapter 10 of the lecture notes in \href{{https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf}}{Computational Physics} or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by \href{{https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html}}{Kristine Baluka Hein and included in the lectures of week 42} at this course are highly recommended.
For the machine learning part you can use your own code from project 2 or the functionality of for example \textbf{Tensorflow/Keras}..
diff --git a/doc/Projects/2021/Project3/pdf/Project3.pdf b/doc/Projects/2021/Project3/pdf/Project3.pdf
index 11b9f8821..12db6a9d8 100644
Binary files a/doc/Projects/2021/Project3/pdf/Project3.pdf and b/doc/Projects/2021/Project3/pdf/Project3.pdf differ
diff --git a/doc/Projects/2021/Project3/pdf/Project3.tex b/doc/Projects/2021/Project3/pdf/Project3.tex
index c436aa4e3..edc852ff6 100644
--- a/doc/Projects/2021/Project3/pdf/Project3.tex
+++ b/doc/Projects/2021/Project3/pdf/Project3.tex
@@ -123,7 +123,7 @@ Project 3 on Machine Learning, deadline December 17, 2021
% --- begin date ---
\begin{center}
-Nov 14, 2021
+Nov 15, 2021
\end{center}
% --- end date ---
@@ -169,7 +169,7 @@ All in all, the report should follow the same pattern as the two previous ones,
We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.
-This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides \href{{https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html}}{from week 43} and/or the textbook by \href{{https://www.springer.com/gp/book/9789401798150}}{Yadav et al}.
+This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides \href{{https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html}}{from week 42} and/or the textbook by \href{{https://www.springer.com/gp/book/9789401798150}}{Yadav et al}.
\textbf{Note}: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise.
@@ -200,7 +200,7 @@ finite difference schemes. We will study the solution of the diffusion
equation in one dimension using a standard explicit scheme and neural
networks to solve the same equations.
-For the explicit scheme, you can study for example chapter 10 of the lecture notes in \href{{https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf}}{Computational Physics} or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by \href{{https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-bs.html}}{Kristine Baluka Hein} at this course are highly recommended.
+For the explicit scheme, you can study for example chapter 10 of the lecture notes in \href{{https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf}}{Computational Physics} or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by \href{{https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html}}{Kristine Baluka Hein and included in the lectures of week 42} at this course are highly recommended.
For the machine learning part you can use your own code from project 2 or the functionality of for example \textbf{Tensorflow/Keras}..
diff --git a/doc/src/Projects/2021/Project3/Project3.do.txt b/doc/src/Projects/2021/Project3/Project3.do.txt
index f4a7dfd22..6e3ce7bc8 100644
--- a/doc/src/Projects/2021/Project3/Project3.do.txt
+++ b/doc/src/Projects/2021/Project3/Project3.do.txt
@@ -31,7 +31,7 @@ All in all, the report should follow the same pattern as the two previous ones,
We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.
-This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides "from week 43":"https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" and/or the textbook by "Yadav et al":"https://www.springer.com/gp/book/9789401798150".
+This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides "from week 42":"https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" and/or the textbook by "Yadav et al":"https://www.springer.com/gp/book/9789401798150".
_Note_: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise.
@@ -69,7 +69,7 @@ finite difference schemes. We will study the solution of the diffusion
equation in one dimension using a standard explicit scheme and neural
networks to solve the same equations.
-For the explicit scheme, you can study for example chapter 10 of the lecture notes in "Computational Physics":"https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf" or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by "Kristine Baluka Hein":"https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-bs.html" at this course are highly recommended.
+For the explicit scheme, you can study for example chapter 10 of the lecture notes in "Computational Physics":"https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf" or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by "Kristine Baluka Hein and included in the lectures of week 42":"https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" at this course are highly recommended.
For the machine learning part you can use your own code from project 2 or the functionality of for example _Tensorflow/Keras_..