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

jupyterp-computation

jupyterp-rethinking

jupyterp-minibatch

jupyterp-dataloader

jupyterp-evaluation

jupyterp-saveload

jupyterp-predictions

jupyterp-training-predictions

jupyterp-checkpointing

jupyterp-classy

jupyterp-classification

jupyterp-retest

jupyterp-retest-copy

jupyterp-retest-copy-copy

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jupyterp-retest-question

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jupyterp-retest-challenge

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jupyterp-putting1

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jupyterp-tensorboard

jupyterp-tensorboard-graphs

jupyterp-tensorboard-scalers

jupyterp-tensorboard-training

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

# Introduction to classification problems

We will now switch to handling different classes of problems known as **classification problems**. In a classification problem, we are trying to predict which class a data point belongs to.

Let us say we have two classes of points; they are either red or blue. These are the labels (`y`) of the points. Sure enough, we need to assign numeric values to them. We could assign zero to red and one to blue. The class associated with zero is the negative class, while one corresponds to the positive class.

In a nutshell, for **binary classification**, we have:

| **Color**  | **Value**  | **Class**  |
|---|---|---|
| Red  | 0  | Negative  |
| Blue  | 1  | Positive |

> **IMPORTANT**: In a classification model, the output is the predicted probability of the positive class. In our case, the model will predict the probability of a point being blue. 

> The choice between which class is positive and which class is negative does not affect model performance. If we reverse the mapping, making red the positive class, the only difference would be that the model would predict the probability of a point being red. But, since both probabilities have to add up to one, we could easily convert between them, so the models are equivalent.

Understand what classification problems are and the data generation and data preparation process involved in it. 

Classification Problems

Understand what classification problems are and the data generation and data preparation process involved in it.

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Classification Problems