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

# Most commonly used metrics

Starting with these four numbers, TN, FP, FN, and TP, you may construct a ton of metrics. We are focusing here on the most commonly used: **true and false-positive rates** (TPR and FPR), **precision**, **recall**, and **accuracy**.

## True and false-positive rates

Let us start with the first two:

<div style="font-size: 1.4em;">
<center>

$TPR = \dfrac{TP}{TP + FN}$ $\space \space FPR = \dfrac{FP}{FP + TN}$

</center>
</div>

For both of them, you divide one value on the right column (positive) by the sum of the corresponding row. So, the true positive rate is computed by dividing the value on the bottom right by the sum of the bottom row. Similarly, the false positive rate is computed by dividing the value on the top right by the sum of the top row. However, what exactly do these mean?

The true positive rate tells you, from all points you _know_ to be positive, how many your model got right. In our example, we know there are 11 positive examples. Our model got ten right. The TPR is 10 out of 11 or roughly 91%. There is yet another name for this metric: **recall**. Makes sense, right? From all the positive examples, how many does your model recall?

# Most commonly used metrics

Starting with these four numbers, TN, FP, FN, and TP, you may construct a ton of metrics. We are focusing here on the most commonly used: **true and false-positive rates** (TPR and FPR), **precision**, **recall**, and **accuracy**.

## True and false-positive rates

Let us start with the first two:

<div>
<center>

$TPR = \dfrac{TP}{TP + FN}$ $\space \space FPR = \dfrac{FP}{FP + TN}$

</center>
</div>

For both of them, you divide one value on the right column (positive) by the sum of the corresponding row. So, the true positive rate is computed by dividing the value on the bottom right by the sum of the bottom row. Similarly, the false positive rate is computed by dividing the value on the top right by the sum of the top row. However, what exactly do these mean?

The true positive rate tells you, from all points you _know_ to be positive, how many your model got right. In our example, we know there are 11 positive examples. Our model got ten right. The TPR is 10 out of 11 or roughly 91%. There is yet another name for this metric: **recall**. Makes sense, right? From all the positive examples, how many does your model recall?

Discover more about the four most commonly used metrics: true and false positive rates, precision, recall and accuracy. 

Metrics

Discover more about the four most commonly used metrics: true and false positive rates, precision, recall and accuracy.

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Metrics

Most commonly used metrics

True and false-positive rates