Morten Hjorth-Jensen
189 lines
7.0 KiB
HTML
189 lines
7.0 KiB
HTML
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doconce format html Project1.do.txt --pygments_html_style=default --html_style=bloodish --html_links_in_new_window --html_output=Project1
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<!-- tocinfo
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{'highest level': 2,
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'sections': [('Format for electronic delivery of report and programs',
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2,
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None,
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'format-for-electronic-delivery-of-report-and-programs'),
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('Software and needed installations',
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None,
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'software-and-needed-installations')]}
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<body>
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<!-- ------------------- main content ---------------------- -->
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<h2 id="format-for-electronic-delivery-of-report-and-programs">Format for electronic delivery of report and programs </h2>
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<p>The preferred format for the report is a PDF file. You can also use DOC or postscript formats or as an ipython notebook file. As programming language we prefer that you choose between C/C++, Fortran2008, Julia or Python. The following prescription should be followed when preparing the report:</p>
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<ul>
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<li> Use Canvas to hand in your projects, log in at <a href="https://www.uio.no/english/services/it/education/canvas/" target="_blank"><tt>https://www.uio.no/english/services/it/education/canvas/</tt></a> with your normal UiO username and password.</li>
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<li> Upload <b>only</b> the report file or the link to your GitHub/GitLab or similar typo of repos! For the source code file(s) you have developed please provide us with your link to your GitHub/GitLab or similar domain. The report file should include all of your discussions and a list of the codes you have developed. Do not include library files which are available at the course homepage, unless you have made specific changes to them.</li>
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<li> In your GitHub/GitLab or similar repository, please include a folder which contains selected results. These can be in the form of output from your code for a selected set of runs and input parameters.</li>
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</ul>
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<p>Finally,
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we encourage you to collaborate. Optimal working groups consist of
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2-3 students. You can then hand in a common report.
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</p>
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<h2 id="software-and-needed-installations">Software and needed installations </h2>
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<p>If you have Python installed (we recommend Python3) and you feel pretty familiar with installing different packages,
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we recommend that you install the following Python packages via <b>pip</b> as
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</p>
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<ol>
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<li> pip install numpy scipy matplotlib ipython scikit-learn tensorflow sympy pandas pillow</li>
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</ol>
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<p>For Python3, replace <b>pip</b> with <b>pip3</b>.</p>
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<p>See below for a discussion of <b>tensorflow</b> and <b>scikit-learn</b>. </p>
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<p>For OSX users we recommend also, after having installed Xcode, to install <b>brew</b>. Brew allows
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for a seamless installation of additional software via for example
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</p>
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<ol>
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<li> brew install python3</li>
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</ol>
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<p>For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution
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you can use <b>pip</b> as well and simply install Python as
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</p>
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<ol>
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<li> sudo apt-get install python3 (or python for python2.7)</li>
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</ol>
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<p>etc etc. </p>
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<p>If you don't want to install various Python packages with their dependencies separately, we recommend two widely used distrubutions which set up all relevant dependencies for Python, namely</p>
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<ol>
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<li> <a href="https://docs.anaconda.com/" target="_blank">Anaconda</a> Anaconda is an open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system <b>conda</b></li>
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<li> <a href="https://www.enthought.com/product/canopy/" target="_blank">Enthought canopy</a> is a Python distribution for scientific and analytic computing distribution and analysis environment, available for free and under a commercial license.</li>
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</ol>
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<p>Popular software packages written in Python for ML are</p>
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<ul>
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<li> <a href="http://scikit-learn.org/stable/" target="_blank">Scikit-learn</a>,</li>
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<li> <a href="https://www.tensorflow.org/" target="_blank">Tensorflow</a>,</li>
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<li> <a href="http://pytorch.org/" target="_blank">PyTorch</a> and</li>
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<li> <a href="https://keras.io/" target="_blank">Keras</a>.</li>
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</ul>
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<p>These are all freely available at their respective GitHub sites. They
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encompass communities of developers in the thousands or more. And the number
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of code developers and contributors keeps increasing.
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</p>
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<!-- ------------------- end of main content --------------- -->
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