deep learning

Hi, In my first post, I looked into the explainability of classical machine learning models. As a next step, I’m interested in the explainability of neural networks. Model explainability is easy for simple models (linear regression, decision trees), and some tools exist for more complex algorithms (ensemble trees). Therefore, I highly recommend the book Interpretable Machine Learning by Christoph Molnar for a deeper theoretical understanding. All different approaches for model explanability are shown with a PyTorch model in this kaggle notebook....

Hi, Recently, I had to work on a simple time-series analysis. I performed poorly since I never worked with time-series before. I believe in a deterministic world, and in general, I prefer to find the causality of a specific data behavior prior to a simple way of empiristic modeling. However, I understand the need for time-series analysis as not enough data available, the underlying processes understood, the complexity bearable, or the time/need for a proper process understanding....

Hi, Recently, I had to work on a simple time-series analysis. I performed poorly since I never worked with time-series before. I believe in a deterministic world, and in general, I prefer to find the causality of a specific data behavior prior to a simple way of empiristic modeling. However, I understand the need for time-series analysis as not enough data available, the underlying processes understood, the complexity bearable, or the time/need for a proper process understanding....