diff --git a/doc/src/week43/exercisesweek43.do.txt b/doc/src/week43/exercisesweek43.do.txt
index 19348228d..3f00420af 100644
--- a/doc/src/week43/exercisesweek43.do.txt
+++ b/doc/src/week43/exercisesweek43.do.txt
@@ -1,35 +1,91 @@
-TITLE: Exercises week 42
+TITLE: Exercises weeks 43 and 44
AUTHOR: October 9-13, 2023
-DATE: Deadline is Sunday October 22 at midnight
+DATE: Deadline is Sunday November 5 at midnight
-You can hand in the exercises from week 41 and week 42 as one exercise and get a total score of two additional points.
+You can hand in the exercises from week 43 and week 44 as one exercise and get a total score of two additional points.
-======= Overarching aims of the exercises this week =======
+======= Overarching aims of the exercises weeks 43 and 44 =======
-The aim of the exercises this week is to get started with implementing
-gradient methods of relevance for project 2. The exercise this week is a simple
-continuation from the previous week with the addition of automatic differentation.
-Everything you develop here will be used in project 2.
-
-In order to get started, we will now replace in our standard ordinary
-least squares (OLS) and Ridge regression codes (from project 1) the
-matrix inversion algorithm with our own gradient descent (GD) and SGD
-codes. You can use the Franke function or the terrain data from
-project 1. _However, we recommend using a simpler function like_
-$f(x)=a_0+a_1x+a_2x^2$ or higher-order one-dimensional polynomials.
-You can obviously test your final codes against for example the Franke
-function. Automatic differentiation will be discussed next week.
-
-You should include in your analysis of the GD and SGD codes the following elements
-o A plain gradient descent with a fixed learning rate (you will need to tune it) using automatic differentiation. Compare this with the analytical expression of the gradients you obtained last week. Feel free to use _Autograd_ as Python package or _JAX_. You can use the examples form last week.
-o Add momentum to the plain GD code and compare convergence with a fixed learning rate (you may need to tune the learning rate). Compare this with the analytical expression of the gradients you obtained last week.
-o Repeat these steps for stochastic gradient descent with mini batches and a given number of epochs. Use a tunable learning rate as discussed in the lectures from week 39. Discuss the results as functions of the various parameters (size of batches, number of epochs etc)
-o Implement the Adagrad method in order to tune the learning rate. Do this with and without momentum for plain gradient descent and SGD using automatic differentiation..
-o Add RMSprop and Adam to your library of methods for tuning the learning rate. Again using automatic differentiation.
-
-The lecture notes from weeks 39 and 40 contain more information and code examples. Feel free to use these examples.
+The aim of the exercises this week and next week is to get started with writing a neural network code
+of relevance for project 2.
-We recommend reading chapter 8 on optimization from the textbook of "Goodfellow, Bengio and Courville":"https://www.deeplearningbook.org/". This chapter contains many useful insights and discussions on the optimization part of machine learning.
+During week 41 we discussed three different types of gates, the
+so-called XOR, the OR and the AND gates. In order to develop a code
+for neural networks, it can be useful to set up a simpler system with
+only two inputs and one output. This can make it easier to debug and
+study the feed forward pass and the back propagation part. In the
+exercise this and next week, we propose to study this system with just
+one hidden layer and two hidden nodes. There is only one output node
+and we can choose to use either a simple regression case (fitting a
+line) or just a binary classification case with the cross-entropy as
+cost function.
+Their inputs and outputs can be
+summarized using the following tables, first for the OR gate with
+inputs $x_1$ and $x_2$ and outputs $y$:
+
+|---------------------|
+| $x_1$ | $x_2$ | $y$ |
+|---------------------|
+| 0 | 0 | 0 |
+| 0 | 1 | 1 |
+| 1 | 0 | 1 |
+| 1 | 1 | 1 |
+|---------------------|
+
+!split
+===== The AND and XOR Gates =====
+
+The AND gate is defined as
+
+|---------------------|
+| $x_1$ | $x_2$ | $y$ |
+|---------------------|
+| 0 | 0 | 0 |
+| 0 | 1 | 0 |
+| 1 | 0 | 0 |
+| 1 | 1 | 1 |
+|---------------------|
+
+And finally we have the XOR gate
+
+|---------------------|
+| $x_1$ | $x_2$ | $y$ |
+|---------------------|
+| 0 | 0 | 0 |
+| 0 | 1 | 1 |
+| 1 | 0 | 1 |
+| 1 | 1 | 0 |
+|---------------------|
+
+!split
+===== Representing the Data Sets =====
+
+Our design matrix is defined by the input values $x_1$ and $x_2$. Since we have four possible outputs, our design matrix reads
+
+!bt
+\bm{X}=\begin{bmatrix} 0 & 0 \\
+ 0 & 1 \\
+ 1 & 0 \\
+ 1 & 1 \end{bmatrix},
+!et
+
+while the vector of outputs is $\bm{y}^T=[0,1,1,0]$ for the XOR gate, $\bm{y}^T=[0,0,0,1]$ for the AND gate and $\bm{y}^T=[0,1,1,1]$ for the OR gate.
+
+
+
+Your tasks here are
+
+o Set up the design matrix with the inputs as discussed above and a vector containing the output, the so-called targets. Note that the design matrix is the same for all gates. You need just to define different outputs.
+o Construct a neural network with only one hidden layer and two hidden nodes using the Sigmoid function as activation function.
+o Set up the output layer with only one output node and use again the Sigmoid function as activation function for the output.
+o Initialize the weights and biases and perform a feed forward pass and compare the outputs with the targets.
+o Set up the cost function (cross entropy for classification of binary cases).
+o Calculate the gradients needed for the back propagation part.
+o Use the gradients to train the network in the back propagation part. Think of using automatic differentiation.
+o Train the network and study your results and compare with results obtained either with _scikit-learn_ or _TensorFlow_.
+
+Everything you develop here can be used directly into the code for the project.
+
diff --git a/doc/src/week43/exercisesweek43.ipynb b/doc/src/week43/exercisesweek43.ipynb
new file mode 100644
index 000000000..31ec7f4c6
--- /dev/null
+++ b/doc/src/week43/exercisesweek43.ipynb
@@ -0,0 +1,169 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "40653618",
+ "metadata": {
+ "editable": true
+ },
+ "source": [
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b661b3a0",
+ "metadata": {
+ "editable": true
+ },
+ "source": [
+ "# Exercises weeks 43 and 44 \n",
+ "**October 9-13, 2023**\n",
+ "\n",
+ "Date: **Deadline is Sunday November 5 at midnight**\n",
+ "\n",
+ "You can hand in the exercises from week 43 and week 44 as one exercise and get a total score of two additional points."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "01487b34",
+ "metadata": {
+ "editable": true
+ },
+ "source": [
+ "# Overarching aims of the exercises weeks 43 and 44\n",
+ "\n",
+ "The aim of the exercises this week and next week is to get started with writing a neural network code\n",
+ "of relevance for project 2. \n",
+ "\n",
+ "During week 41 we discussed three different types of gates, the\n",
+ "so-called XOR, the OR and the AND gates. In order to develop a code\n",
+ "for neural networks, it can be useful to set up a simpler system with\n",
+ "only two inputs and one output. This can make it easier to debug and\n",
+ "study the feed forward pass and the back propagation part. In the\n",
+ "exercise this and next week, we propose to study this system with just\n",
+ "one hidden layer and two hidden nodes. There is only one output node\n",
+ "and we can choose to use either a simple regression case (fitting a\n",
+ "line) or just a binary classification case with the cross-entropy as\n",
+ "cost function.\n",
+ "\n",
+ "Their inputs and outputs can be\n",
+ "summarized using the following tables, first for the OR gate with\n",
+ "inputs $x_1$ and $x_2$ and outputs $y$:\n",
+ "\n",
+ "\n",
+ "\n",
+ "| $x_1$ | $x_2$ | $y$ |
|---|
\n",
+ "\n",
+ "\n",
+ "| 0 | 0 | 0 |
\n",
+ "| 0 | 1 | 1 |
\n",
+ "| 1 | 0 | 1 |
\n",
+ "| 1 | 1 | 1 |
\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e8f2df30",
+ "metadata": {
+ "editable": true
+ },
+ "source": [
+ "## The AND and XOR Gates\n",
+ "\n",
+ "The AND gate is defined as\n",
+ "\n",
+ "\n",
+ "\n",
+ "| $x_1$ | $x_2$ | $y$ |
|---|
\n",
+ "\n",
+ "\n",
+ "| 0 | 0 | 0 |
\n",
+ "| 0 | 1 | 0 |
\n",
+ "| 1 | 0 | 0 |
\n",
+ "| 1 | 1 | 1 |
\n",
+ "\n",
+ "
\n",
+ "\n",
+ "And finally we have the XOR gate\n",
+ "\n",
+ "\n",
+ "\n",
+ "| $x_1$ | $x_2$ | $y$ |
|---|
\n",
+ "\n",
+ "\n",
+ "| 0 | 0 | 0 |
\n",
+ "| 0 | 1 | 1 |
\n",
+ "| 1 | 0 | 1 |
\n",
+ "| 1 | 1 | 0 |
\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a3d25110",
+ "metadata": {
+ "editable": true
+ },
+ "source": [
+ "## Representing the Data Sets\n",
+ "\n",
+ "Our design matrix is defined by the input values $x_1$ and $x_2$. Since we have four possible outputs, our design matrix reads"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "abdf765d",
+ "metadata": {
+ "editable": true
+ },
+ "source": [
+ "$$\n",
+ "\\boldsymbol{X}=\\begin{bmatrix} 0 & 0 \\\\\n",
+ " 0 & 1 \\\\\n",
+ "\t\t 1 & 0 \\\\\n",
+ "\t\t 1 & 1 \\end{bmatrix},\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "684ff136",
+ "metadata": {
+ "editable": true
+ },
+ "source": [
+ "while the vector of outputs is $\\boldsymbol{y}^T=[0,1,1,0]$ for the XOR gate, $\\boldsymbol{y}^T=[0,0,0,1]$ for the AND gate and $\\boldsymbol{y}^T=[0,1,1,1]$ for the OR gate.\n",
+ "\n",
+ "Your tasks here are\n",
+ "\n",
+ "1. Set up the design matrix with the inputs as discussed above and a vector containing the output, the so-called targets. Note that the design matrix is the same for all gates. You need just to define different outputs.\n",
+ "\n",
+ "2. Construct a neural network with only one hidden layer and two hidden nodes using the Sigmoid function as activation function.\n",
+ "\n",
+ "3. Set up the output layer with only one output node and use again the Sigmoid function as activation function for the output.\n",
+ "\n",
+ "4. Initialize the weights and biases and perform a feed forward pass and compare the outputs with the targets.\n",
+ "\n",
+ "5. Set up the cost function (cross entropy for classification of binary cases).\n",
+ "\n",
+ "6. Calculate the gradients needed for the back propagation part.\n",
+ "\n",
+ "7. Use the gradients to train the network in the back propagation part. Think of using automatic differentiation.\n",
+ "\n",
+ "8. Train the network and study your results and compare with results obtained either with **scikit-learn** or **TensorFlow**.\n",
+ "\n",
+ "Everything you develop here can be used directly into the code for the project."
+ ]
+ }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}