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

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jupyterp-retest-question

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jupyterp-retest-question-copy-copy

jupyterp-retest-question-copy-copy-copy

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jupyterp-retest-challenge

jupyterp-retest-challenge-copy

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jupyterp-putting1

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jupyterp-putting3

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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 gradients

A gradient is a **partial derivative**; why partial? Because one computes it with respect to (w.r.t.) a single parameter. Since we have two parameters, `b` and `w`, we must compute two partial derivatives.

A **derivative** tells you how much a given quantity changes when you slightly vary some other quantity. In our case, how much does our MSE loss change when we vary each one of our two parameters separately?

# Introduction to gradients

A gradient is a **partial derivative**; why partial? Because one computes it with respect to (w.r.t.) a single parameter. Since we have two parameters, `b` and `w`, we must compute two partial derivatives.

A **derivative** tells you how much a given quantity changes when you slightly vary some other quantity. In our case, how much does our MSE loss change when we vary each one of our two parameters separately?

Learn about gradients and how they can be computed and visualized.

First Two Steps in Using Gradient Descent

Step 3 - Compute the Gradients

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Step 3 - Compute the Gradients

Introduction to gradients