In this last part, we outline the key learnings from this project, including key paths to success and common pitfalls to avoid.
In this Part IV we explore the MMM methodology applied.
In this Part III we explore the methodology for using Tesseract to detect text in creative images.
In this Part II we explore the methodology for training Detectron2 models to detect brand-specific object in creative images.
In this Part I we explore the methodology for using pre-trained Detectron2 models to detect brand-specific object in creative images.
Two illustrations of how attention coefficients can be a source of interpretability.
Two illustrations of how attention coefficients can be a source of interpretability
Hi everyone, we are now in September and we release our 6th Newsletter! Ranging from podcasts to tutorials, this Newsletter is made for practicioners!
This article is part of a series of articles which will introduce several optimization techniques, from traditional (yet advanced) Mathematical Optimization solvers and associated packages to Deep Reinforcement Learning algorithms, while tackling a very famous Operations Research problem: the multi-knapsack problem. Here, the focus is on an approach based on two famous reinforcement learning algorithms: Q-Learning and Policy Gradient.