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A Comprehensive Guide to PyTorch Tensors: From Basics to Advanced Operations
Latest   Machine Learning

A Comprehensive Guide to PyTorch Tensors: From Basics to Advanced Operations

Last Updated on March 13, 2024 by Editorial Team

Author(s): Fatma Elik

Originally published on Towards AI.

Photo by Sebastian Coman Photography on Unsplash

Unlock PyTorch tensor mastery!U+2728 From basics to advanced operations, elevate your Deep Learning skills with this comprehensive guide. U+1F525

Overview of the Course Structure U+1F9F5

Introduction to TensorsCreating TensorsRetrieving Information from TensorsManipulating TensorsHandling Tensor ShapesMatrix Multiplication in Depth

To be a master in Deep Learning topics, one should know tensor multiplications deeply. Pytorch workflow is already designed to serve this purpose and in my opinion, this path may beneficial.

Basics of TensorsImportance in Machine Learning and Deep Learning

Deep Learning is based on all about matrix multiplication as it is known. Artificial neural networks are calculated through tensor operations, particularly matrix multiplication. Well, what is the difference between matrices and tensors?

Image sourced from Deep Learning Book Series by Hadrien Jean.

A matrix is a 2-dimensional array, meaning it has a row and a column, and can be considered a 2nd-order tensor. On the other hand, a tensor has a number of dimensions and will have higher orders.


0 Rank tensors are Scalars1st Rank tensors are 1-D arrays2nd Rank tensors are 2-D arrays (A matrix)nth Rank tensors are n-D arrays (A Tensor)

Neural Networks may be calculated using different data formats, such as numerical, text, geospatial, image, audio, and video data. Dealing with… Read the full blog for free on Medium.

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