DMG installer: Install the app by dragging it into the Applications folder.Zip file: Extract the contents of the file to your location of choice, then double-click CurrentTask.exe to open the app.The application will show up in the list of installed applications and will be added to the Start menu. MSI installer: Requires administrator access.Installing and upgrading Installing the application Only specifically scheduled work after 20:00.Warning when missing start of scheduled task.Draw attention to current task every 5 minutes, either by nagging or blinking.Break every 30 minutes, clear current task when break starts.Show error if no single current task, start nagging after 60 seconds, reset if idle for 5 minutes.Nagging, blinking and downtime conditions.Conditions dealing with numerical values.If you can’t run Jupyter Notebooks on your own device or you are very RAM-restricted, we would recommend having a Google account so that you can use Google Colab for free notebook access.Current version: 1.4.2 (for a history of releases and details regarding what they include, take a look at the GitHub releases page)įor installation instructions, see Installing and upgrading Documentation If you haven’t heard of “embeddings”, “tokenizer” and “transformer models” yet you should still be able to follow the workshop. You do not have to bring prior knowledge regarding Natural Language Processing, but doing so certainly helps. Setting up the environment before the session helps us in focussing our time on the fun parts! We will have hosted the necessary files on a GitHub page that also contains installation instructions for setting up a virtual python environment. The libraries we use are all written in Python, so you should be comfortable with writing Python code and know yourself around in a Jupyter notebook. Since this is a hands-on workshop, you should bring your own laptop or have a friend with whom you can share the session. The six categories are:īuilding a fast and minimal backend using FastAPIįinishing up with the streamlit dashboard We prepare the data for the key steps beforehand so that a person could still follow the workshop even though they do not manage to code everything in time. We divided the use case into six categories in which we each will first build a theoretical foundation and then dive into the hands-on coding part. The workshop is also accompanied by theoretical concepts to help you better understand the contents and applications of common NLP techniques.īy the end of the workshop, you will have an understanding of some fundamental NLP concepts and will be able to apply these to build your own applications using only python with popular frameworks like streamlit or fastAPI. All of this will then come together in a comprehensive Streamlit application. Within 3 hours we will scrape a prepared inbox (you can also apply the techniques to your own one), clean the texts, convert them into embedding vectors and craft a feature matrix that will be used for content-based recommendations. We will build a personalized and extendable newsletter dashboard from a given E-Mail-Collection. This workshop will take you through an application of NLP in the real world.
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