Flipboard

FEBRUARY 2, 2023

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.

Cross Validation 123

Cross Validation ML ML Machine Learning 123

AWS Machine Learning Blog

FEBRUARY 10, 2023

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.

ML 100

ML ML Machine Learning Machine Learning 100

Heartbeat

JUNE 14, 2023

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.

Deep Learning 59

Deep Learning Deep Learning Support Vector Machines Machine Learning 59

Mlearning.ai

JANUARY 31, 2023

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.

Clustering 52

Clustering Algorithm Machine Learning Machine Learning 52

Mlearning.ai

FEBRUARY 2, 2023

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.,

Cross Validation 52

Cross Validation Deep Learning Deep Learning Artificial Intelligence 52