At version r1.5, Google's open source machine learning and neural network library is more capable, more mature, and easier to learn and use If you looked at TensorFlow as a deep learning framework ...

torch A tensor library like ... create an acyclic graph to be solved in a session, the way TensorFlow works by default. While I wouldn’t rush to convert existing deep learning projects to ...

Tensors are useful for deep learning as it involves a lot of neural networks and neural network calculations (forward and backward propagation) can be better represented as computational graph. The ...

A tensor is the ... a stateful dataflow graph, which is similar to a flowchart that remembers past events, to perform operations on the data in these tensors. TensorFlow is known for being a ...

We’ve talked about TensorFlow before — Google’s deep learning library. Crunching all that data is the province of big computers, not embedded systems, right? Not so fast. [Neil-Tan] and ...

However, it is a PyTorch-like lightweight deep learning framework ... Any contribution to the repo is welcome. The library has a built-in auto-differentiation engine that dynamically builds a ...

and from graphs to recommendation system, have established deep learning as the state of the art in machine learning and data analytics alike. At the core of deep learning models lies the concept of a ...

Learning from complex, multidimensional data has become central to computational mathematics ... of related work combining tensors and deep learning. In Section 3, we give the background notation on ...

Abstract: In intelligent transportation systems, deep learning is a widely adopted technique for traffic ... In this paper, we propose a communication-efficient Graph-Tensor Fast Iterative ...