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<section id="workshops">
<h1>Workshops<a class="headerlink" href="#workshops" title="Permalink to this headline">#</a></h1>
<p>For course description and to register, please visit: <a class="reference external" href="https://idre.ucla.edu/events">https://idre.ucla.edu/events</a></p>
<p>2022 Summer</p>
<ul class="simple">
<li><p>Learning Scikit-Learn</p></li>
<li><p>High Performance Machine Learning Using Scikit-Learn</p></li>
</ul>
<p>2022 Spring</p>
<ul class="simple">
<li><p>Introduction to Google Cloud Platform. Part 1: Overview</p></li>
<li><p>Introduction to Google Cloud Platform. Part 2: Compute engine and practice</p></li>
<li><p>Boosting Python for High Performance Data Analytics (1) Interpreter War</p></li>
<li><p>Boosting Python for High Performance Data Analytics (2) DataFrame Game</p></li>
<li><p>Data Visualization with Python I: Plotting Fundamentals</p></li>
<li><p>Data Visualization with Python II: Making Interactive Plots and Widgets</p></li>
<li><p>Job Scheduling on Hoffman2 Cluster</p></li>
</ul>
<p>2022 Winter</p>
<ul class="simple">
<li><p>Version Control with Git</p></li>
<li><p>Make and Makefiles</p></li>
<li><p>Tips to run Matlab on Hoffman2 cluster</p></li>
<li><p>Converting plots from Matlab to Python/matplotlib</p></li>
<li><p>Practical Parallel Computing Part 1: Running MPI Programs</p></li>
<li><p>Practical Parallel Computing Part 2: MPI Programming</p></li>
<li><p>Practical Parallel Computing Part 3: Introduction to PETSc</p></li>
<li><p>Learning Convolutional Neural Networks (1)</p></li>
<li><p>Learning Convolutional Neural Networks (2)</p></li>
<li><p>Learning Generative Adversarial Networks</p></li>
<li><p>Scientific Visualization with Paraview</p></li>
</ul>
<p>2021 Fall</p>
<ul class="simple">
<li><p>Docker containerization: Fundamentals</p></li>
<li><p>Docker containerization: Practices</p></li>
<li><p>Introduction to SQL</p></li>
<li><p>Job scheduling on Hoffman2 Cluster</p></li>
<li><p>Using SQL with Python for Data Analysis</p></li>
<li><p>Deep Learning, the Good, the Bad and the Ugly</p></li>
<li><p>Learning Deep Learning Mechanics</p></li>
<li><p>Learning PyTorch</p></li>
<li><p>Learning Convolutional Neural Networks</p></li>
<li><p>Common Data formats</p></li>
</ul>
<p>2021 Summer</p>
<ul class="simple">
<li><p>Introduction to the Linux Shell: Using the Command Line</p></li>
<li><p>Introduction to the Linux Shell: Shell Scripting</p></li>
<li><p>Workflow automation with continuous integration and deployment (CI/CD)</p></li>
<li><p>Topics in Scientific Computing with Julia</p></li>
<li><p>Data visualization from matlab to python matplotlib</p></li>
<li><p>Learning Scikit-Learn: the basics</p></li>
<li><p>Learning Scikit-Learn: advanced topics</p></li>
<li><p>An In-Depth Introduction to Google Colab</p></li>
</ul>
<p>2021 Spring</p>
<ul class="simple">
<li><p>Data Visualization with Python I: Plotting Fundamentals</p></li>
<li><p>Data Visualization with Python II: Making Interactive Plots and Widgets</p></li>
<li><p>Singularity on Hoffman2: Using containers on HPC resources</p></li>
<li><p>Package management in Julia</p></li>
<li><p>High-Performance Data Science in Python (1) Interpreter War</p></li>
<li><p>High-Performance Data Science in Python (2) DataFrame Game</p></li>
<li><p>Data Visualization with Julia</p></li>
<li><p>Introduction to Virtual machines and Containers</p></li>
</ul>
<p>2021 Winter</p>
<ul class="simple">
<li><p>Parallel computing using MPI and Julia</p></li>
<li><p>Numerical computing using Julia</p></li>
<li><p>Using Anaconda to mange packages on Hoffman2 Cluster</p></li>
<li><p>Introduction to Virtual Machines and Containers</p></li>
<li><p>Learning Scikit-Learn</p></li>
<li><p>Numerical Computing with Python: Python Basics</p></li>
<li><p>Numerical Computing with Python: Intro to Numpy, Scipy, and Pandas</p></li>
<li><p>Numerical Computing with Python: Numerical Modeling</p></li>
</ul>
<p>2020 Fall</p>
<ul class="simple">
<li><p>Jupyter Advanced Topics</p></li>
<li><p>Make and Makefiles</p></li>
<li><p>Advanced Graphics with Matlab</p></li>
<li><p>Learning Deep Learning with PyTorch (1) Introduction</p></li>
<li><p>Learning Deep Learning with PyTorch (2) Mechanics of Learning</p></li>
<li><p>Learning Deep Learning with PyTorch (3) Knowing PyTorch</p></li>
<li><p>Learning Deep Learning with PyTorch (4) Convolutional Neural Networks</p></li>
<li><p>Learning Deep Learning with PyTorch (5) Improving CNNs Performance</p></li>
<li><p>Learning Deep Learning with PyTorch (6) Generative Adversarial Networks</p></li>
<li><p>[Markdown for technical writing](<a class="reference external" href="https://github.com/schuang/markdown-for-technical-writing">https://github.com/schuang/markdown-for-technical-writing</a>)</p></li>
</ul>
</section>
</article>
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