Tensor Operations
Tensor operations are mathematical computations performed on tensors, which are multi-dimensional arrays that generalize scalars, vectors, and matrices to higher dimensions. They are fundamental in fields like machine learning, physics, and engineering, enabling efficient manipulation of data in neural networks, scientific simulations, and numerical analysis. Common operations include addition, multiplication, reshaping, and specialized functions like convolution or matrix decomposition.
Developers should learn tensor operations when working with machine learning frameworks (e.g., TensorFlow, PyTorch) to build and optimize models, as they form the backbone of neural network computations like forward and backward propagation. They are also essential in scientific computing for simulations, data analysis, and high-performance computing tasks that involve large-scale numerical data, improving efficiency and scalability in these domains.