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
python
Import
python
Tensor Types
Simple Tensor Creation
python
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:
python
- 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:
python
- 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:
python
Tensor Axes
Applications of Tensors
- Table Data
- Text Sequences
- Numerical Sequences
- Image Processing
- Video Processing
Batches
Tensor Creation Methods
python
Convertions
- NumPy to Tensor
python
- Pandas to Tensor
python
- Constant Tensor to a Variable Tensor
python
Data Types
python
Arithmetic
- Addition
python
- Subtraction
python
- Element-wise Multiplication
python
- Division
python
Broadcasting
Linear Algebra
- Matrix Multiplication
python
- Matrix Inversion
python
- Transpose
python
- Dot Product
python
Reshape
python
Slicing
python
Modifying with Slicing
python
Concatenating
python
Reduction Operations
python
Gradient Tape
python
@tf.function
python
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