👉 Tensorship is a concept in the field of computer science and machine learning, particularly in the context of neural networks. It refers to a type of network architecture that allows for the representation of multiple inputs and outputs simultaneously, which can be useful for tasks such as image recognition or natural language processing. In a traditional neural network, each input (or feature) is represented by a single weight vector, while each output is also represented by a single weight vector. This means that when two different inputs are fed
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What is the definition of Tensors? 🙋
👉 Tensors are mathematical objects that represent quantities that can be added, multiplied, and integrated over a space or time. They are fundamental to many fields in mathematics, physics, and engineering, and play a crucial role in various applications such as robotics, signal processing, and machine learning. The concept of tensors was introduced by Italian mathematician Carl Friedrich Gauss in the 19th century and is named after him because it combines the concepts of vectors and scalars into one unified object. The basic definition
