Deep learning with Pytorch, A beginner's Guide By Daniel Godoy.png

jupyter_final.tar.gz

codesnip1

tensorboard

jupyterp-autograd

jupyterp-computation

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jupyterp-minibatch

jupyterp-dataloader

jupyterp-evaluation

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jupyterp-checkpointing

jupyterp-classy

jupyterp-classification

jupyterp-retest

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GUI-Tensorboard

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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

## General overview

First of all, congratulations are in order! You have successfully implemented a fully functioning model and training loop in PyTorch!

# General overview

First of all, congratulations are in order! You have successfully implemented a fully functioning model and training loop in PyTorch!

A quick overview of what you have learned and testing it on the notebook provided.

Recap

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Recap

General overview