10 Deadly Sins of Machine Learning Model Training

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10 Deadly Sins of Machine Learning Model Training. ML model training is the most time-consuming and resource-expensive part of the overall model-building journey. Training by definition is iterative, but somewhere during the iterations, mistakes seep into the mix. In this article, I share the ten deadly sins during ML model training — these are the most common as well as the easiest to overlook.

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A Machine Learning Model monitoring checklist: 7 things to track

ML model checklist

It is not easy to build a machine learning model. It is even harder to deploy a service in production. But even if you managed to stick all the pipelines together, things do not stop here. Once the model is in use, we immediately have to think about operating it smoothly. We need to make sure the model delivers. It means we need to monitor our models. And there are more things to look for!

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Multidimensional multi-sensor time-series data analysis framework

Multidimensional multi-sensor time-series data analysis framework. In this blog post, I will take you through my package “msda” useful for time-series sensor data analysis. A quick introduction about time-series data is also provided. The demo notebook can be found on here. One of the specific use case applications focused on “Unsupervised Feature Selection” using the package can be found in the blog post here.

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My machine learning model does not learn. What should I do?

If you work with data in general, and machine learning algorithms in particular, you might be familiar with that feeling of frustration when a model really does not want to learn the task at hand. You have tried it all, but the accuracy metric just won’t rise. What next? Where is the problem? Is this an unsolvable task or is there a solution somewhere you’re not aware of?

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Adversarial attacks on explainable AI

There are various adversarial attacks on machine learning models; hence, ways of defending, e.g. by using Explainable AI methods. Nowadays, attacks on model explanations come to light, so does the defense to such adversary. Here, we introduce fundamental concepts related to the domain. When considering an explanation as a function of model and data, there is a possibility to change one of these variables to achieve a different result.

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Essential math for Data Science: Introduction to matrices and the matrix product

As you saw in Essential Math for Data Science, vectors are a useful way to store and manipulate data. You can represent them geometrically as arrows, or as arrays of numbers (the coordinates of their ending points). However, it can be helpful to create more complicated data structures – and that is where matrices need to be introduced.

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Top 10 Computer Vision papers 2020

Even with everything that happened in the world this year, we still had the chance to see a lot of amazing research come out. Especially in the field of artificial intelligence and more precisely computer vision. Artificial intelligence and our understanding of the human brain and its link to AI is constantly evolving, showing promising applications in the soon future, which I will definitely cover. Here are my top 10 of the most interesting research papers of the year in computer vision, in case you missed any of them.

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