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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

Flipboard

With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. We then explain the details of the ML methodology and model training procedures.

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Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. In this post, we deep dive into the technical details of this ML model.

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Calibration Techniques in Deep Neural Networks

Heartbeat

Advances in Neural Information Processing Systems 33 (2020): 15288–15299. [10] Cross Validated] Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. 9] Mukhoti, Jishnu, et al.

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What's your cardiovascular age?

Mlearning.ai

The use of Jupyter Notebooks was done in order to make it possible to train and validate the models on Google Colab in order to get access to free GPUs. doing cross-validation on the training set and a mean absolute error of 8.3 Data Min Knowl Disc 34 , 1936–1962 (2020). years on the test set. Ismail Fawaz et al.,

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Intuitive robotic manipulator control with a Myo armband

Mlearning.ai

The test runs a 5-fold cross-validation. There, you will find a quick notebook on which you can test the performance of an SVM on the data annotated with both the labels “by hand” and the labels provided by the K-means. As you can see, using hand-made labels, the SVM performs quite well. We are in the nearby of 0.9 2657–2666, Nov.