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

# Model

Given a **classification problem**, one of the most straightforward models is **logistic regression**. But, instead of simply presenting it and using it right away, we are going to build up to it. The rationale behind this approach is twofold. First, it will clarify why this algorithm is called logistic regression if it is used for classification. Second, you will have a clear understanding of what a **logit** is.

## Linear regression

Well, since it is called "logistic regression," then **linear regression** is a good starting point for us to build up to it. What would a linear regression model with two features look like?

<div style="font-size: 1.6em;">
<center>

$y = b + w_1 x_1 + w_2 x_2 + \epsilon$

</center>
</div>

There is one obvious problem with the model above. Our labels (`y`) are discrete, meaning they can be only zero or one. We need to change the model slightly to adapt it to our purposes.

> "What if we assign the positive outputs to one and the negative outputs to zero?"

Makes sense, right? We are already calling them positive and negative classes anyway. Why not put their names to good use? Our model would look like this:

<div style="font-size: 1.6em;">
<center>

$$ y = \left\{
\begin{array}{ll}
      1, & if \space\space b + w_1x_1 + w_2x_2 \geq 0 \\
      0, & if \space\space b + w_1x_1 + w_2x_2 < 0
\end{array}
\right. $$

</center>
</div>

An example of this can be seen below:

# Model

Given a **classification problem**, one of the most straightforward models is **logistic regression**. But, instead of simply presenting it and using it right away, we are going to build up to it. The rationale behind this approach is twofold. First, it will clarify why this algorithm is called logistic regression if it is used for classification. Second, you will have a clear understanding of what a **logit** is.

## Linear regression

Well, since it is called "logistic regression," then **linear regression** is a good starting point for us to build up to it. What would a linear regression model with two features look like?

<div>
<center>

$y = b + w_1 x_1 + w_2 x_2 + \epsilon$

</center>
</div>

There is one obvious problem with the model above. Our labels (`y`) are discrete, meaning they can be only zero or one. We need to change the model slightly to adapt it to our purposes.

Makes sense, right? We are already calling them positive and negative classes anyway. Why not put their names to good use? Our model would look like this:

<div>
<center>

$$ y = \left\{
\begin{array}{ll}
      1, & if \space\space b + w_1x_1 + w_2x_2 \geq 0 \\
      0, & if \space\space b + w_1x_1 + w_2x_2 < 0
\end{array}
\right. $$

</center>
</div>

An example of this can be seen below:

Model for Classification Problems

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Model for Classification Problems

Model

Linear regression