The goal of the workshop is to give a step-by-step guide to build a robust deep neural network for bioimage analysis, enabling participants to apply key concepts of deep learning, without the need for coding skills. Additionally, the workshop aims to foster a critical perspective on the use and training of deep neural networks in order to gain a comprehensive understanding of the potential and limitations of these techniques. For this purpose, we will use a user-friendly Jupyter Notebook to illustrate the essential components, from data preparation to results analysis, including the training/finetuning on Google Colab. Common pitfalls will be emphasized to ensure the best possible outcome. Through this workshop, various bioimage analysis applications (segmentation, denoising…) will be demonstrated both on synthetic and provided real-world datasets. Prerequisites: basic knowledge in supervised machine and deep learning.