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Neural Networks

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title: Neural Networks
categories: session

The material in this page is meant for 3-4 sessions.
You should be content if you do one exercise in a day; some may
be able to complete two in a day.

+ [Briefing 10 November](ANN)
+ [Briefing 11 November](CNN)

# Reading

+ [PyTorch Quickstart](
+ [Learn the Basics](
    + see also the tutorial under Exercise 1. below
+ Szeliski 2022 Chapter 5

# Exercise 1. Basic tutorial.

I have added a couple of exercises to the official
[PyTorch Quickstart](

+ Download and open the (augmented) [tutorial](ann-tutorial.ipynb).
+ Please reflect upon and discuss the questions.

# Exercise 2. Convolutional Neural Networks

1. Complete the [CIFAR-10 Tutorial](
2. Compare the approach to Exercise 1.  What is different?
   What is the same?
# Exercise 3. Regression.

One in-house project is to classify images of remote galaxies,
which are often distorted due to the gravity of dark matter.
This effect is known as gravitational lensing.

A sample dataset can be found at
This directory contains

+ A data file, `sphere-pm.csv` 
+ 10000 images of distorted galaxies
+ A python file `` defining a subclass of `Dataset`
  to manage these data

The CSV file has the form
The interesting columns are the filename which points to the
input image,  and
the four output variables $x$, $y$, `einsteinR`, and $\sigma$.
The other columns are associated with more advanced problem instances
and should be ignored.

1.  Study the Dataset class ``.  How is the dataset managed?
2.  Use this class to test if you can train a network to determine the
    four outputs for an image.

# Exercise 4.  Own Data (Optional)

Making your own training data may be difficult, because of the
large quantity of data that you need, but if you want to give it
a try, I would recommend this. 

1.  Team up with everybody else who wants to do this problem.
2.  Each team member makes a set of hand-drawn digits, at least
    ten versions of each digit each.
3.  Digitise the digits into image files.  Make sure that everybody
    uses the same resolution.  
    + The resolution must be large enough to make digits recognisable,
      but small enough to save computational power.
    + Somewhere between $28\times28$ (same as the MIST dataset)
      and $64\times64$ may be considered.
4.  Gather the images into a dataset with their labels (digit value).
    You may use Exercise 3 as an example.
5.  Merge and share the dataset.
6.  Train and test a neural network to read the digits.
7.  Try different networks, at least the ones you have used in previous

# Exercise 5.  More examples (optional)

Other datases may be found at 
[this collection]( 
if you want to try other variants.