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

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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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How To Improve Machine Learning Model Accuracy

DagsHub

In 2018, there were extensive news reports that an Uber self-driving car made an accident with a pedestrian in Tempe, Arizona. The pedestrian died, and investigators found that there was an issue with the machine learning (ML) model in the car, so it failed to identify the pedestrian beforehand. Read more about benchmarking ML models.

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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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Machine Learning Strategies Part 07: Addressing Bias and Variance

Mlearning.ai

For example, if you are using regularization such as L2 regularization or dropout with your deep learning model that performs well on your hold-out-cross-validation set, then increasing the model size won’t hurt performance, it will stay the same or improve. The only drawback of using a bigger model is computational cost. Ng, Andrew.

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What a data scientist should know about machine learning kernels?

Mlearning.ai

This is often done using techniques such as cross-validation or grid search. Hyperparameter tuning is the process of finding the optimal values for these hyperparameters to maximize the performance of the algorithm. There are several approaches to hyperparameter tuning for kernel-based algorithms, including: 1.