What are tensors in deep learning?
An example of an AI chatbot conversation about deep learning tensors.
What are tensors in deep learning?
Tensors are multi-dimensional arrays that are used to represent data in deep learning. They generalize scalars, vectors, and matrices.
How do tensors differ from regular arrays?
Tensors can have more than two dimensions, allowing them to represent complex data structures, while regular arrays are typically limited to one or two dimensions.
Can you give me an example of a tensor?
Sure! A 3D tensor could represent a color image, where the dimensions are height, width, and color channels (RGB).
How are tensors used in neural networks?
Tensors are used to store inputs, weights, and outputs in neural networks, enabling efficient computation and data flow.
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