A Tensor is essentially a multi-dimensional array, much like the ones handled by NumPy, a popular Python library. While they share similarities, there are some key differences that set them apart:
GPU vs. CPU
NumPy is primarily designed to run on CPUs, utilizing single or multi-threaded CPU operations. In contrast, Tensors are built to take advantage of GPUs for parallel computing and can even run on specialized hardware like Google’s TPUs. This makes Tensors better suited for handling complex computations and is why they are a natural fit for deep learning tasks.
Mutability
NumPy arrays are mutable, meaning you can directly modify their elements. Tensors, on the other hand, are immutable—you can’t change their values directly. However, if you define a Tensor as a variable, you can update it.
Eager and Graph Execution
NumPy operates in a straightforward, procedural manner, executing operations immediately. Tensors in frameworks like TensorFlow support both eager execution (similar to NumPy) and graph execution. In graph execution, operations are structured as part of a computational graph that can be optimized for efficient execution—a critical feature for deep learning models.
In summary, while both NumPy and Tensors handle multi-dimensional arrays, Tensors provide more flexibility and performance for large-scale, complex computations, especially in machine learning and deep learning workflows.
Thanks
Sreeni Ramadorai
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