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

jupyterp-retest-copy-copy-copy

jupyterp-retest-copy-copy-copy-copy

jupyterp-retest-question

jupyterp-retest-question-copy

jupyterp-retest-question-copy-copy

jupyterp-retest-question-copy-copy-copy

jupyterp-retest-question-copy-copy-copy-copy

jupyterp-retest-challenge

jupyterp-retest-challenge-copy

jupyterp-retest-challenge-copy-copy

jupyterp-retest-challenge-copy-copy-copy

jupyterp-retest-challenge-copy-copy-copy-copy

jupyterp-putting1

jupyterp-putting2

jupyterp-putting3

jupyterp-putting4

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

# Different types of methods

In the last lesson, we introduced the methods `_make_train_step` and `_make_val_step` and noticed these methods having an underscore as a prefix. Why is this so and how is this different from the double underscore in the `__init__` method?"

Let us take a look at why this is the case. 

## Public, protected, and private methods

Some programming languages like Java have three kinds of methods: public, protected, and private. 

**Public methods** are the kind you are most familiar with; they can be called by the user. 

**Protected methods**, on the other hand, should not be called by the user. They are supposed to be called either internally or by the child class (the child class can call a protected method from its parent class).

Finally, **private methods** are supposed to be called exclusively internally. They should be invisible even to a child class.

These rules are strictly enforced in Java, but Python takes a more relaxed approach; all methods are public. Meaning, you can call whatever method you want. But you can suggest the appropriate usage by prefixing the method name with a single underscore (for protected methods) or a double underscore (for private methods). This way, the user is aware of the programmer's intention.

In our example, both `_make_train_step` and `_make_val_step` are defined as protected methods. It is expected by us for users to not call them directly, but if someone decides to define a class that inherits from `StepByStep`, they should feel entitled to do so.

To make the additions to our code visually simpler, that is, without having to replicate the full class every time a new method is introduced, I am resorting to something that should not be used in regular circumstances: [`setattr`](https://www.w3schools.com/python/ref_func_setattr.asp). 

Learn about the different types of method types along with an overview of what setattr is and how it works. 

General Class Terms

Learn about the different types of method types along with an overview of what setattr is and how it works.

Different Method Types

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Different Method Types

Different types of methods

Public, protected, and private methods