Delve into PyTorch basics: autograd, model classes, datasets, and data loaders. Gain insights into model development while avoiding common pitfalls. Start creating and training your own PyTorch models.

Deep learning with Pytorch, A beginner's Guide By Daniel Godoy.png

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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.

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

# Diving into tensors

First, we need to cover a few basic concepts that may throw you off balance if you do not grasp them well enough before going full-force on modeling.

In Deep Learning, we see **tensors** everywhere. Well, Google’s framework is called TensorFlow for a reason! What is a tensor, anyway?

# Tensor

In Numpy, you may have an array that has three dimensions, right? That is, technically speaking, a tensor.

> A **scalar** (a single number) has zero dimensions, a vector has one dimension, a matrix has two dimensions, and a tensor has three or more dimensions. That’s it!

Learn about what tensors are, and look at a few of its built-in functions.

PyTorch Tensors

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

PyTorch Tensors