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update introduction notebook

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ana deligny 2023-09-25 00:34:38 +01:00
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# PyBuilder
target/
# Jupyter notebook
*/.ipynb_checkpoints/*.ipynb

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notebooks/.ipynb_checkpoints/01_Introduction-checkpoint.ipynb
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"\n",
"Today's exercises are meant to allow you to get some initial familiarisation with the `mlp` package and how data is provided to the learning functions. Next week onwards, we will follow with the material covered in lectures. \n",
"\n",
"If you are new to Python and/or NumPy and are struggling to complete the exercises, you may find going through [this Stanford University tutorial](http://cs231n.github.io/python-numpy-tutorial/) by [Justin Johnson](http://cs.stanford.edu/people/jcjohns/) first helps. There is also a derived Jupyter notebook by [Volodymyr Kuleshov](http://web.stanford.edu/~kuleshov/) and [Isaac Caswell](https://symsys.stanford.edu/viewing/symsysaffiliate/21335) which you can download [from here](https://github.com/kuleshov/cs228-material/raw/master/tutorials/python/cs228-python-tutorial.ipynb) - if you save this in to your `mlpractical/notebooks` directory you should be able to open the notebook from the dashboard to run the examples.\n",
"If you are new to Python and/or NumPy and are struggling to complete the exercises, you may find going through [this Stanford University tutorial](http://cs231n.github.io/python-numpy-tutorial/) by Justin Johnson first helps. There is also a derived Jupyter notebook by Volodymyr Kuleshov and Isaac Caswell which you can download [from here](https://github.com/kuleshov/teaching-material/blob/master/tutorials/python/cs228-python-tutorial.ipynb) - if you save this in to your `mlpractical/notebooks` directory you should be able to open the notebook from the dashboard to run the examples.\n",
"\n",
"## Data providers\n",
"\n",
"Open (in the browser) the [`mlp.data_providers`](../../edit/mlp/data_providers.py) module. Have a look through the code and comments, then follow to the exercises.\n",
"Open (in the browser) the [`mlp.data_providers`](../mlp/data_providers.py) module. Have a look through the code and comments, then follow to the exercises.\n",
"\n",
"### Exercise 1 \n",
"\n",
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"source": [
"### Exercise 3\n",
"\n",
"Here you will write your own data provider `MetOfficeDataProvider` that wraps [weather data for south Scotland](http://www.metoffice.gov.uk/hadobs/hadukp/data/daily/HadSSP_daily_qc.txt). A previous version of this data has been stored in `data` directory for your convenience and skeleton code for the class provided in `mlp/data_providers.py`.\n",
"Here you will write your own data provider `MetOfficeDataProvider` that wraps weather data for south Scotland. A previous version of this data has been stored in `data` directory for your convenience and skeleton code for the class provided in `mlp/data_providers.py`.\n",
"\n",
"The data is organised in the text file as a table, with the first two columns indexing the year and month of the readings and the following 31 columns giving daily precipitation values for the corresponding month. As not all months have 31 days some of the entries correspond to non-existing days. These values are indicated by a non-physical value of `-99.9`.\n",
"\n",