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tpmmthomas 2024-09-18 22:43:30 +08:00
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notebooks/01_Introduction.ipynb
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"source": [ "source": [
"# Exercises\n", "# Exercises\n",
"\n", "\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. You are going to implement variants of a `DataProvider` class, which preprocesses data and serves data in batches when the `__next__()` function is called. \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. You are going to implement variants of a `DataProvider` class, which preprocesses data and serves data in batches when the `next()` function is called. \n",
"\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 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", "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", "\n",