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 dynamic computation graph

> “Unfortunately, no one can be told what the dynamic computation graph is. You have to see it for yourself." 
<br/><br/>
> -&nbsp;[Morpheus](https://i.imgflip.com/3ft45b.jpg)

How great was _The Matrix_? Right? Right? But jokes aside, we want you to see the graph for yourself too!

The [PyTorchViz](https://github.com/szagoruyko/pytorchviz) package and its `make_dot(variable)` method allow us to easily visualize a graph associated with a given Python variable involved in the gradient computation.

## Plotting graphs of tensors

So, let us stick with the bare minimum; two (gradient computing) tensors for our parameters, predictions, errors, and loss. These are Steps 0, 1, and 2.

# Introduction to dynamic computation graph

How great was _The Matrix_? Right? Right? But jokes aside, we want you to see the graph for yourself too!

The [PyTorchViz](https://github.com/szagoruyko/pytorchviz) package and its `make_dot(variable)` method allow us to easily visualize a graph associated with a given Python variable involved in the gradient computation.

# Plotting graphs of tensors

So, let us stick with the bare minimum; two (gradient computing) tensors for our parameters, predictions, errors, and loss. These are Steps 0, 1, and 2.

Dynamic Computation Graphs

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Dynamic Computation Graphs

Introduction to dynamic computation graph