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

# BCELoss

Sure enough, PyTorch implements the binary cross-entropy loss, [`nn.BCELoss`]. Just like its regression counterpart, `MSELoss` (introduced in the chapter, **A Simple Regression Problem**), it is a higher-order function that returns the actual loss function.

The **BCELoss** higher-order function takes two optional arguments (the others are deprecated, and you can safely ignore them):

- `reduction`: It takes either `mean`, `sum`, or `none`. The default `mean` corresponds to our equation 6.15 in the previous lesson. As expected, `sum` will return the sum of the errors instead of the average. The last option, `none`, corresponds to the unreduced form; that is, it returns the full array of errors.

- `weight`: The default is `none`. Meaning, every data point has equal weight. If informed, it needs to be a tensor with a size that equals the number of elements in a mini-batch, representing the weights assigned to each element in the batch. In other words, this argument allows you to assign different weights to each element of the current batch based on its position. So, the first element would have a given weight, the second element would have a different weight, and so on. This is regardless of the actual class of that particular data point. Sounds confusing? Weird? Yes, this is weird. Of course, this is not useless or a mistake, but the proper usage of this argument is a more advanced topic and outside the scope of this course.

Uncover the different ways you can compute the binary cross-entropy loss in PyTorch. 

Binary Cross-Entropy Loss in PyTorch

Uncover the different ways you can compute the binary cross-entropy loss in PyTorch.

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Binary Cross-Entropy Loss in PyTorch

BCELoss