Workshop

We are happy to announce, that on 23th of Mai 2019, the deep.TEACHING team will offer a free workshop.

ai-workshop

Facts

Topic: Machine Learning in Practice
Date: 23th of May 2019 Time: 10:00 - 17:00
Address: HTW Berlin / CBMI - Ostendstraße 25, 12459 Berlin, Haus 1A
Language: German
Application Deadline: 3rd of May 2019

Contact: Klaus.Strohmenger@HTW-Berlin.de

Hardware: bring a notebook capable for semi heavy calculations. A mobile i5 gen. 2xxx and upwards, 8 GByte of RAM should be sufficient.
Software: your setup should either be prepared to run docker files (recommended) or natively have the listed packages installed.

Download Exercises and Solution

The exercises for the workshop can be downloaded here

Program

Subject to change

10:00 Welcoming and Presentation of the deep.TEACHING Project
10:30 Lectures
  • What is Deep Learning?
  • Deep Learning Libraries and Frameworks
 Exercises (Subject to change)
  • Introduction into PyTorch
13:00 Lunch Break
14:00 Excercises (Subject to change)
  • Linear Regression (predicting float values)
  • Logistic Regression (simple classifier)
  • Fully Connected Neural Network (classifier)
17:00 Farewell

What You Get

  • A mixture of interesting lectures and practical experience

What You Give

  • Your time, ideas and dedication

FAQ

How do I sign up?

Workshop participant slots are limited. If you are interested write an e-mail to Klaus.Strohmenger@HTW-Berlin.de.

Who is the targeted audience?

The course is aimed towards beginners in machine learning and those who want to get in touch with it. Though you should have basic experience in (scientific) python.

I do not want to use docker. Which software packages are required to natively run the notebooks?

Note: This is a provisional list and still subject to change. Please revisit this site one week before the workshop.

  • Python3.6 or greater
  • Jupyter Lab or Jupyter Notebook

  • And the following Python packages:

    • numpy
    • matplotlib
    • pytorch
    • torchvision
    • pandas
    • scikit-learn
    • sklearn
    • scipy

How do I check if my setup is working correctly?

You can test if you have the right packages installed by downloading executing the following Jupyter Notebook file. If no exception is thrown, everything should be fine, though we recommend to also prepare your notebook with docker just in case.

Further, installation instructions for the notebooks can be found here.

What can I do to prepare myself before taking part in the workshop?

For better learning experience we suggest refreshing your Python and numpy skills with this Jupyter Notebook exercise.

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