Tensor Data Structures in Deep Learning Frameworks
Tensor data structures in deep learning frameworks constitute a mathematical foundation for representing multi-dimensional arrays (tensors) within the domain of linear algebra and computational neuroscience applications. The core principle involves defining objects through rank, shape, dtype, and device placement properties to enable efficient batched operations via parallel computation models such as GPU acceleration or tensor processing units (TPUs). This concept operates strictly within theoretical computer science applied machine learning theory, serving as a formal mechanism for encoding vector spaces required for differentiable programming.
Tensors in Neural Networks: Data Storage and Automatic Differentiation using GPUs/TPUs
Tensors in machine learning serve as N-dimensional arrays designed to facilitate hardware acceleration via GPUs and TPUs for efficient data storage and automatic differentiation during backpropagatio…