How does memory management work in TensorFlow.js?
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How does memory management work in TensorFlow.js?
Memory management in TensorFlow.js involves efficient allocation and deallocation of tensors to optimize performance.
What are tensors and why are they important?
Tensors are multi-dimensional arrays that are the core data structure in TensorFlow, essential for computations.
How can I monitor memory usage in my TensorFlow.js application?
You can use the tf.memory() function to get insights into memory usage and manage resources effectively.
Are there any best practices for managing memory in TensorFlow.js?
Yes, some best practices include disposing of tensors when no longer needed and using tf.tidy() to manage scope.
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