Might have a minimal bias towards Deep Learning — Have you ever asked yourself where you currently are on your Machine Learning journey? And what’s there that you can still learn about? This checklist helps you answer such questions. It provides an outline of the field, divided into three broad levels: Entry level (where everybody starts), intermediate level (where…

Examine the effect of augmentations in your browser — When working with image data, practitioners often use augmentations. Augmentations are techniques that artificially and randomly alter the data to increase diversity. Applying such transformations to the training data makes the model more robust. For image data, frequently used candidates are rotating, resizing, or blurring. The effects of the transformations…

A guide to the evolution of generative networks — Introduction In short, the core idea behind generative networks is capturing the underlying distribution of the data. This distribution can not be observed directly, but has to be approximately inferred from the training data. Over the years, many techniques have emerged that aim to generate data similar to the input samples. …

Using native Python packages only — Manually creating a novel configuration file for every new experiment is a tedious process. Especially if you want to rapidly deploy a vast number of jobs on a Kubernetes cluster, an automated setup is a must. With python, it’s straightforward to build a simple scheduling script that reads an experiment’s…

When I was a beginner using Kubernetes, my main concern was getting code to run on the cluster. Thrown into a new world, I saw all these confusing YAML-Files, each line and indentation bringing a new meaning. Once I learned the fastest way to get code into the file, I…

Fix the input ordering by using seeds — When preparing machine learning experiments, the input pipeline plays a critical role in data preparation. While they are often straightforward to construct — the machine learning frameworks make this relatively easy —, they lack reproducibility. This is by default: randomness in the input data, such as applying shuffling after each…

My Ph.D. story: Issue Three — The night before my first tutoring session — with me as a tutor, not as a student — I was very nervous. I conjured up all kinds of questions I could be asked and which I would not know the answers to. I imagined spilling coffee over my materials; I…

My Ph.D. Story: Issue Two — Two months ago, I began my Ph.D. in Machine Learning. To give others a glimpse into this — sometimes far removed — academic world, I created a monthly blog post/newsletter. Through my day-to-day experience in research, I can offer you my unique “hands-on” perspective on academia and related topics. …

Setup and settlement — A month ago, I began my Ph.D. in Machine Learning. To give others a glimpse into this — sometimes far removed — academic world, I created a monthly blog post/newsletter. Through my day-to-day experience in research, I can offer you my unique “hands-on” perspective on academia and related topics. Now…