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

## What to expect from this chapter

In this chapter, we will:

- Build a model for **binary classification**.

- Understand the concept of **logits** and how it is related to probabilities.
- Use **binary cross-entropy loss** to train a model.
- Use the loss function to handle **imbalanced datasets**.
- Understand the concepts of **decision boundary** and **separability**.
- Learn how the **choice of a classification threshold** impacts evaluation metrics.
- Build **ROC** and **precision-recall** curves.

## Imports

For the sake of organization, all libraries needed throughout the code and used in any given chapter are imported at the very beginning. For this chapter, we will need the following imports:

# What to expect from this chapter

In this chapter, we will:

- Build a model for **binary classification**.

- Understand the concept of **logits** and how it is related to probabilities.
- Use **binary cross-entropy loss** to train a model.
- Use the loss function to handle **imbalanced datasets**.
- Understand the concepts of **decision boundary** and **separability**.
- Learn how the **choice of a classification threshold** impacts evaluation metrics.
- Build **ROC** and **precision-recall** curves.

# Imports

For the sake of organization, all libraries needed throughout the code and used in any given chapter are imported at the very beginning. For this chapter, we will need the following imports:

Learn about the learning outcomes of this chapter. 

Spoilers

Learn about the learning outcomes of this chapter.

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Spoilers

What to expect from this chapter