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. Next, we present the data preprocessing and other transformation methods applied to the dataset.

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. Advances in neural information processing systems 32 (2019).

ML 77

ML ML Machine Learning Machine Learning 77

AWS Machine Learning Blog

NOVEMBER 27, 2023

While this data holds valuable insights, its unstructured nature makes it difficult for AI algorithms to interpret and learn from it. According to a 2019 survey by Deloitte , only 18% of businesses reported being able to take advantage of unstructured data. Clean data is important for good model performance.

Data Preparation 90

Data Preparation AI AI Python 90

Alation

FEBRUARY 1, 2022

And those who practice these “old school” governance methods have little confidence in their efficacy: 73% of Ventana research participants stated that spreadsheets were a data governance concern for their organization, while 59% viewed incompatible tools as the top barrier to a single source of truth. And it’s growing in popularity.

Data Lakes 52

Data Lakes Data Governance ML ML 52

AWS Machine Learning Blog

NOVEMBER 30, 2023

AWS innovates to offer the most advanced infrastructure for ML. For ML specifically, we started with AWS Inferentia, our purpose-built inference chip. Neuron plugs into popular ML frameworks like PyTorch and TensorFlow, and support for JAX is coming early next year. Customers like Adobe, Deutsche Telekom, and Leonardo.ai

AWS 139

AWS AI AI ML 139

Mlearning.ai

JANUARY 26, 2023

It can be gradually “enriched” so the typical hierarchy of data is thus: Raw data ↓ Cleaned data ↓ Analysis-ready data ↓ Decision-ready data ↓ Decisions. For example, vector maps of roads of an area coming from different sources is the raw data. Data, 4(3), 92. Data, 4(3), 94.

AWS 98

AWS Database Data Science Clean Data 98

Flipboard

JULY 10, 2023

It works well for simple data but may struggle with complex patterns. Figure 4: Architecture of fully connected autoencoders (source: Amor, “Comprehensive introduction to Autoencoders,” ML Cheat Sheet , 2021 ).

Deep Learning 131

Deep Learning Deep Learning Machine Learning Machine Learning 131