Tensor Basics: Understand Tensor or Learn from Websites

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In summary, a tensor is a mathematical object used to describe geometric relationships between different sets of data. It is commonly used in machine learning and deep learning algorithms, as well as in physics and engineering. Tensors have multiple dimensions and can represent a wide range of data, from scalars and vectors to matrices and higher-dimensional arrays. To learn more about tensors, there are many helpful resources available online, including websites that provide detailed explanations and examples. Understanding tensors is essential for anyone interested in data analysis, artificial intelligence, or scientific research.
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could someone please explain tensors to me or at least tell of website that explaines them
 
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http://www.geocities.com/r-sharipov/r4-b6.htm
Quick intro to tensor analysis-- probably the simplest I've found.
It's very easy to comprehend, it just depends on your background. (Especially with linear algebra, obviously)
Enjoy.
 
  • #3
search this subforum (there is a search function), the question 'what is a tensor?' comes up regularly here, you will find some nice explanations
 
  • #4
i am also trying to learn it

there are some websites that i find useful (especially if you are also interested inphysics):

http://pancake.uchicago.edu/~carroll/notes/

http://people.hofstra.edu/faculty/Stefan_Waner/diff_geom/tc.html

http://www.cs.elte.hu/geometry/csikos/dif/dif.html

http://vishnu.mth.uct.ac.za/omei/gr/info.html


http://ocw.mit.edu/OcwWeb/Physics/8-962Spring2002/LectureNotes/index.htm --->>(robphy's recommendation in another discussion)

Also i have to warn you about notation some of them may not be agree in notation so it is better to choose one of them
 
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FAQ: Tensor Basics: Understand Tensor or Learn from Websites

1. What is a tensor?

A tensor is a mathematical object used to represent and manipulate multi-dimensional data. It is a generalization of scalars (0-dimensional), vectors (1-dimensional), and matrices (2-dimensional) to higher dimensions.

2. What are the key properties of tensors?

Tensors have two main properties: rank and shape. Rank refers to the number of dimensions of a tensor, while shape describes the size of each dimension. Tensors also have data type, such as integer or float, and can be either dense or sparse.

3. How are tensors used in machine learning and data science?

Tensors are used extensively in machine learning and data science for their ability to efficiently represent and manipulate large and complex datasets. They are used as the primary data structure for neural networks and deep learning algorithms.

4. What is the difference between a tensor and a matrix?

A matrix is a special case of a tensor, specifically a 2-dimensional tensor. Tensors can have any number of dimensions, while matrices are limited to two. Additionally, tensors can have different data types and shapes, while matrices are typically limited to numerical data and rectangular shapes.

5. Where can I learn more about tensors?

There are many websites and resources available for learning about tensors, including online courses, tutorials, and textbooks. Some popular websites for learning about tensors include TensorFlow's official documentation, DeepLearning.ai, and Fast.ai.

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