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Release 0.1.0

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After refurbishing the project we prepare a new relaease.
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Alexander Hess 2024-04-08 22:13:31 +02:00
commit 94e5112f10
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04_iteration/00_content.ipynb
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@ -8,7 +8,7 @@
}
},
"source": [
"**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/00_content.ipynb)."
"**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/04_iteration/00_content.ipynb)."
]
},
{
@ -874,7 +874,7 @@
"\n",
"To understand this in detail, we have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
"\n",
"Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb#Memoization), after introducing the `dict` data type.\n",
"Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/02_content.ipynb#Memoization), after introducing the `dict` data type.\n",
"\n",
"Let's measure the average run times for `fibonacci()` and varying `i` arguments with the `%%timeit` [cell magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit) that comes with Jupyter."
]