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

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tensorboard

jupyterp-autograd

jupyterp-computation

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jupyterp-dataloader

jupyterp-evaluation

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jupyterp-checkpointing

jupyterp-classy

jupyterp-classification

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GUI-Tensorboard

GUI-Tensorboard-graphs

GUI-Tensorboard-scalers

GUI-Tensorboard-training

GUI-Chapter04

GUI-No-Jupyter-Tensorboard

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

## Best and worst curves

Let us ask ourselves: what would the best possible (and the worst possible) curves look like?

The best curve belongs to a model that predicts everything perfectly; it gives us a 100% probability to all actual positive data points and 0% probability to all actual negative data points. Of course, such a model does not exist in real life. But cheating does exist. So, let us cheat and use the true labels as the probabilities. These are the curves we get:

# Best and worst curves

Let us ask ourselves: what would the best possible (and the worst possible) curves look like?

The best curve belongs to a model that predicts everything perfectly; it gives us a 100% probability to all actual positive data points and 0% probability to all actual negative data points. Of course, such a model does not exist in real life. But cheating does exist. So, let us cheat and use the true labels as the probabilities. These are the curves we get:

Understand the best and worst possible curves as well as the comparison between the best and worst models. 

Best +  Worst Curves and Models

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Best + Worst Curves and Models

Best and worst curves