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

jupyter_final.tar.gz

codesnip1

tensorboard

jupyterp-autograd

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jupyterp-checkpointing

jupyterp-classy

jupyterp-classification

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GUI-Tensorboard

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GUI-Chapter04

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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

# Overall view of the result

In this chapter, we have not modified the training pipeline much. The data preparation part is roughly the same as in the previous chapter except that we performed the split using Scikit-Learn this time. The model configuration part is primarily the same as well, but we changed the loss function, so it is appropriate now for a classification problem. The model training part is quite straightforward since the development of the `StepByStep` class in the last chapter.

But now, after training a model, we can use our class' `predict` method to get predictions for our validation set, and use Scikit-Learn's `metrics` module to compute a wide range of classification metrics like the confusion matrix.

Behold your pipeline: 

## Data preparation

The major difference from the previous version was that we used a dataset that is already split into training and validation sets. This can also be observed in the given code below:

Let's combine all the processes together and see the final output.

Putting it All Together

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Putting it All Together

Overall view of the result