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Chollet Discusses the Types of Tensors Typically Encountered in Deep \bullet

question 55

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Chollet discusses the types of tensors typically encountered in deep learning: \bullet A 0D (0-dimensional) tensor is one value and is known as a xe "scalar value"scalar.
\bullet A 1D tensor is similar to a one-dimensional array and is known as a xe "vector"vector. A 1D tensor might represent a sequence, such as hourly temperature readings from a sensor or the words of one movie review.
\bullet A 2D tensor is similar to a two-dimensional array and is known as a xe "matrix"matrix. A 2D tensor could represent a grayscale image in which the tensor's two dimensions are the image's width and height in pixels, and the value in each element is the intensity of that pixel.
Which of the following statements a) , b) or c) about additional types of tensors is false?


Definitions:

Ceteris Paribus

A Latin phrase meaning "all other things being equal," used in economics to isolate the effect of one variable by holding other relevant factors constant.

Income Increases

Situations or events that lead to a rise in the amount of money received by individuals or entities, potentially affecting consumption and saving behaviors.

Ceteris Paribus

A Latin phrase meaning "all other things being equal," used in economics to analyze the effect of one variable on another while holding other relevant factors constant.

Economic Variables

Quantitative measures that represent a characteristic of the economy or a part of the economy, influencing or describing economic activity.

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