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

jupyter_final.tar.gz

codesnip1

tensorboard

jupyterp-autograd

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GUI-Tensorboard

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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