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

# Introduction to layers

A `Linear` model can be seen as a **layer** in a neural network.

In the example above, the **hidden layer** would be `nn.Linear(3, 5)` (since it takes 3 inputs from the input layer, and generates 5 outputs), and the **output layer** would be `nn.Linear(5, 1)` (since it takes 5 inputs, the outputs from the hidden layer, and generates a single output).

If we use `Sequential` to build it, it looks like this:

Learn about what layers are, how you can name them, and the different types of layers that are present. 

Layers

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Layers

Introduction to layers