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

## Step 0 - Random initialization

In our example, we already know the **true values** of the parameters, but this will obviously never happen in real life. If we knew the true values, why even bother to train a model to find them?

Given that we will never know the true values of the parameters, we need to set **initial values** for them. However, how do we choose them? 

It turns out, a random guess is as good as any other. For training a model, you need to randomly initialize the parameters/weights. In our case, we only have two: `b` and `w`.

This can be observed in the code given below:

# Step 0 - Random initialization

In our example, we already know the **true values** of the parameters, but this will obviously never happen in real life. If we knew the true values, why even bother to train a model to find them?

Given that we will never know the true values of the parameters, we need to set **initial values** for them. However, how do we choose them? 

It turns out, a random guess is as good as any other. For training a model, you need to randomly initialize the parameters/weights. In our case, we only have two: `b` and `w`.

This can be observed in the code given below:

Learn about the first two steps for using gradient descent.  

Basic Steps for Using Gradient Descent (Step 0 and 1)

Learn about the first two steps for using gradient descent.

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Basic Steps for Using Gradient Descent (Step 0 and 1)

Step 0 - Random initialization