Today i passed the Google Professional Machine Learning Engineer Exam in a onsite test center in Duesseldorf. I prepared for it 4 weeks with different ressources. I have multiple years experience on Data Science and ML and about one year experience with ML on GCP. Following article should summarize all sources which were helpful for me in preparation of the certification exam. If you are interested in how the exam is actually happening there are plenty of other articles on medium or at other places....

Storytelling and presentation are the keys to a succesful adoption of your ideas. While storytelling is often verbal, a presentation is what stays and often is the thing that decideds whether your stakeholders pay attention or not so much. So it has to be aesthetic, clear and simple. I show a lot of ideas and often i tend to recreate stuff from my code documentation enriched with diagrams. It is often a tedious work, recreating things in Powerpoint....

TL;DR The following learning notes try to give some intuition on how Stable Diffusion works, some mathematical intuition on Diffusion Models and an introduction to Latent Diffusion Models. The following sources were extensively used during creation of this learning notes. Passages may be reutilized to create a reasonable overview of the topic. Credit goes to the authors of the following papers and posts. Illustrations by Jay Alammar Sohl-Dickstein et al., 2015...

Prerequitises To test the models here you need to have an account with HuggingFace - for loading the checkpoint or using the endpoints. Hugging Face is a community and data science platform that provides: Tools that enable users to build, train and deploy ML models based on open source (OS) code and technologies. A place where a broad community of data scientists, researchers, and ML engineers can come together and share ideas, get support and contribute to open source projects....

This summary is based on another post: An introduction to ChatGPT written by ChatGPT While testing out ChatGPT for some weeks now, i found that texts created by it are often repetitive and monotonous. In this post i tried to condense the meaningful information from the other post. 2022 was the year of generative AI. Generative AI refers to machine learning algorithms that can create new meaning from text, images, code, and other forms of content....