The efficiency of transformers in machine learning refers to their ability to process and generate data with minimal computational resources while maintaining high performance. This efficiency is achieved through mechanisms like self-attention and parallelization, which allow transformers to handle large datasets and complex tasks effectively.

Efficiency Of Transformers

efficiency of transformers

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience. It leverages data to train models that can make predictions or decisions without being explicitly programmed for specific tasks.

machine learning

ability to process and generate data

minimal computational resources

high performance

Self-attention is a mechanism in neural networks that allows the model to weigh the importance of different words in a sentence relative to each other, enabling it to capture long-range dependencies and contextual relationships. It forms the backbone of Transformer architectures, which have revolutionized natural language processing tasks by allowing for efficient parallelization and improved performance over sequential models.

self-attention

Parallelization is the process of dividing a computational task into smaller, independent tasks that can be executed simultaneously across multiple processors or cores, thereby reducing the overall execution time. It is a fundamental technique in high-performance computing and is essential for efficiently utilizing modern multi-core and distributed computing architectures.

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