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The Machine Learning “Advent Calendar” Day 5: GMM in Excel

Within the previous article, we explored distance-based clustering with K-Means. further: to enhance how the gap could be measured we add variance, with the intention to get the Mahalanobis distance. So, if k-Means is the...

The Machine Learning “Advent Calendar” Day 4: k-Means in Excel

4 of the Machine Learning Advent Calendar. Through the first three days, we explored distance-based models for supervised learning: In all these models, the thought was the identical: we measure distances, and we resolve the...

The Best Data Scientists are At all times Learning

it’s possible to totally master every topic in data science? With data science covering such a broad range of areas — statistics, programming, optimization, experimental design, data storytelling, generative AI, to call a couple...

The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel

the k-NN Regressor and the thought of prediction based on distance, we now take a look at the k-NN Classifier. The principle is identical, but classification allows us to introduce several useful variants, reminiscent...

The Machine Learning “Advent Calendar” Day 1: k-NN Regressor in Excel

to this “Advent Calendar” of Machine learning and deep learning in Excel. For Day 1, we start with the k-NN (k-Nearest Neighbors) regressor algorithm. And as you will notice, this is absolutely the best...

Learning, Hacking, and Shipping ML

You wrote many beginner and explanatory articles on TDS. Has teaching the basics modified the way you design or debug real systems at work? I notice the correlation between the more I teach something, the...

The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint

, it is rather easy to coach any model. And the training process is at all times done with the seemingly same method fit. So we get used to this concept that training any...

Learning Triton One Kernel at a Time: Softmax

Within the previous article of this series, operation in all fields of computer science: matrix multiplication. It's heavily utilized in neural networks to compute the activation of linear layers. Nevertheless, activations on their...

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