Зміст курсу
Introduction to TensorFlow
Introduction to TensorFlow
2. Basics of TensorFlow
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
Import
Tensor Types
Simple Tensor Creation
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:
- 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:
- Types: Tensors come in various data types. While there are many, some common ones include
float32
,int32
, andstring
. You can get the the data type of the tensor by the.dtype
attribute:
Tensor Axes
Applications of Tensors
- Table Data
- Text Sequences
- Numerical Sequences
- Image Processing
- Video Processing
Batches
Tensor Creation Methods
Convertions
- NumPy to Tensor
- Pandas to Tensor
- Constant Tensor to a Variable Tensor
Data Types
Arithmetic
- Addition
- Subtraction
- Element-wise Multiplication
- Division
Broadcasting
Linear Algebra
- Matrix Multiplication
- Matrix Inversion
- Transpose
- Dot Product
Reshape
Slicing
Modifying with Slicing
Concatenating
Reduction Operations
Gradient Tape
@tf.function
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