Let's now summarize all the key topics we've discussed in this course. Feel free to download the overview material in the end of this page.

Tensorflow Set Up

Instalation

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Import

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Tensor Types

Simple Tensor Creation

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Tensor Properties

• Rank : It tells you the number of dimensions present in the tensor. For instance, a matrix has a rank of 2 . You can get the rank of the tensor using the .ndim attribute:

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• Shape : This describes how many values exist in each dimension. A 2x3 matrix has a shape of (2, 3) . The length of the shape parameter matches the tensor's rank (its number of dimensions) . You can get the the shape of the tensor by the .shape attribute:

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• Types : Tensors come in various data types. While there are many, some common ones include float32 , int32 , and string . You can get the the data type of the tensor by the .dtype attribute:

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Tensor Axes

Applications of Tensors

• Table Data

• Text Sequences

• Numerical Sequences

• Image Processing

• Video Processing

Batches

Tensor Creation Methods

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Convertions

• NumPy to Tensor

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• Pandas to Tensor

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• Constant Tensor to a Variable Tensor

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Data Types

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Arithmetic

• Addition

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• Subtraction

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• Element-wise Multiplication

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• Division

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Broadcasting

Linear Algebra

• Matrix Multiplication

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• Matrix Inversion

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• Transpose

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• Dot Product

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Reshape

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Slicing

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Modifying with Slicing

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Concatenating

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Reduction Operations

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Gradient Tape

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@tf.function

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