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Вивчайте Summary | Basics of TensorFlow
Introduction to TensorFlow
course content

Зміст курсу

Introduction to TensorFlow

Introduction to TensorFlow

1. Tensors
2. Basics of TensorFlow

book
Summary

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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question mark

What role does a loss function play in a neural network?

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