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

# TensorBoard introduction

TensorBoard is a very useful visualization tool from PyTorch's competing framework, Tensorflow. And you can use this with PyTorch as well, which provides classes and methods for us to integrate TensorBoard with our model.

# Running TensorBoard inside a notebook

First, we need to load Tensorboard's extension for Jupyter:

# Load the TensorBoard notebook extension
%load_ext tensorboard

Then, we run Tensorboard using the newly available magic:

# Running the TensorBoard notebook extension
%tensorboard --logdir runs

The magic above tells TensorBoard to look for logs inside the folder specified by the `logdir` argument: `runs`. So, in this case, there must be a `runs` folder in the same location as the notebook you are using to train the model. To make things easier for you, the `runs` folder was already created beforehand, so you get it out-of-the-box already.

Learn about what TensorBoard is, how you can run it on your Jupyter notebook, and how to test and run it. 

TensorBoard

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

TensorBoard

TensorBoard introduction

Running TensorBoard inside a notebook