The ability to work with tensors is fundamental in order to build models that can analyze and make decisions on large datasets. Two of the most popular tools for this purpose are PyTorch which was ...

PyTorch provides a set of abstractions and features that can help build more complex models, with support for tensors and neural networks. Tensors make it easy to work with large multidimensional ...

Various arithmetic operations include tensor vector and tensor matrix products, element-wise operations, dot products, convolution, concatenation ... tensors with TTrank R = 20, physical dimension ...

When different forces cannot be condensed into a single stress, we have to describe the overall system. An excellent way to do this is through tensors. Tensors are an elegant mathematical method to ...

Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...

Training details for a cINN to concatenate two autoencoders from different image ... Note that the BigBiGAN class is just a naive wrapper, which converts pytorch tensors to numpy arrays, feeds them to ...

Dr. James McCaffrey of Microsoft Research presents the fundamental concepts of tensors necessary to establish a solid foundation for learning how to create PyTorch neural networks, based on his ...