Deep Learning with PyTorch Step-by-Step: Part I - Fundamentals
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Deep Learning with PyTorch Step-by-Step: Part I - Fundamentals


This course is designed to provide you with an easy-to-follow, structured, incremental, and from-first-principles approach to learning PyTorch. In this course, you’ll be introduced to the fundamentals of PyTorch: autograd, model classes, datasets, data loaders, and more. 

You will develop, step-by-step, not only the models themselves but also your understanding of them. You'll be shown both the reasoning behind the code and how to avoid some common pitfalls and errors along the way. By the time you finish this course, you’ll have a thorough understanding of the concepts and tools necessary to start developing and training your own models using PyTorch.

What is PyTorch? 

PyTorch is a popular, open source, optimized tensor library widely used in deep learning and AI Research, developed by researchers at Facebook AI. The torch package contains data structures for multi-dimensional tensors and mathematical operations over these are defined.

In this blog post, we seek to cover some useful functions that the torch package provides for manipulating tensors. Specifically, we'll take the help of examples to understand how the different functions work, including cases where the functions do not perform as expected and throw errors. We shall look at the following tensor manipulation functions.

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