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

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jupyterp-retest-challenge-copy-copy-copy

jupyterp-retest-challenge-copy-copy-copy-copy

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

## Running TensorBoard

We have already looked at how TensorBoard can be run inside a notebook. Now, we will be taking a look at how you can run TensorBoard locally as well as on Binder. 

## Running TensorBoard separately (local installation)

Assuming you've installed TensorBoard while following the **Setup Guide**, now you need to open a new terminal or Anaconda Prompt, navigate to the `PyTorchStepByStep` folder you cloned from GitHub, and activate the `pytorchbook` environment:

# Running TensorBoard

We have already looked at how TensorBoard can be run inside a notebook. Now, we will be taking a look at how you can run TensorBoard locally as well as on Binder. 

# Running TensorBoard separately (local installation)

Assuming you've installed TensorBoard while following the **Setup Guide**, now you need to open a new terminal or Anaconda Prompt, navigate to the `PyTorchStepByStep` folder you cloned from GitHub, and activate the `pytorchbook` environment:

Learn how to run TensorBoard in your specific environment. 

Setup Guide

Learn how to run TensorBoard in your specific environment.

Running TensorBoard

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Running TensorBoard

Running TensorBoard

Running TensorBoard separately (local installation)