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

# Introduction to models

In PyTorch, a **model** is represented by a regular Python class that inherits from the [`Module`](https://bit.ly/32MhAj8) class.

> **IMPORTANT**: Are you comfortable with **object-oriented programming (OOP)** concepts like **classes**, **constructors**, **methods**, **instances**, and **attributes**? 
<br/><br/>
> If you are unsure about any of these terms, we strongly recommend that you first brush up your skills in OOP before proceeding. 
<br/><br/>
> Having a good understanding of OOP is key to benefit the most from PyTorch's capabilities.

So, assuming you are already comfortable with OOP, let us dive into developing a model in PyTorch.

The most fundamental methods a model class needs to implement are:

- `__init__(self)`: It defines the parts that make up the model; in our case, two parameters of `b` and `w`.

> You are not limited to defining parameters though. Models can contain other models as their attributes as well, so you can easily nest them. We will see an example of this shortly as well.
<br/><br/>
> Besides, do not forget to include `super().__init__()` to execute the `__init__()` method of the **parent class** (`nn.Module`) before your own.

Learn about the model class and the different types of functions that come along with it. 

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Model

Introduction to models