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

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jupyterp-retest-question

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jupyterp-tensorboard

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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

# Introduction to epoch

Before we continue our process, let us explore what exactly is an **epoch** and when it gets completed since we will be using this later on.
 
## Definition

The number of epochs is a hyper-parameter that refers to the number of complete iterations of the algorithm being used through the training set. 

An epoch is complete whenever every point in the training set (`N`) has already been used in all steps: **forward pass, computing loss, computing gradients**, and **updating parameters**. 

## Updates and gradient descent 

During one epoch, we perform at least one update, but no more than `N` updates. The number of updates (`N/n`) will depend on the type of gradient descent being used:

* For batch (`n = N`) gradient descent, this is trivial, as it uses all points for computing the loss; one epoch is the same as one update.

* For stochastic (`n = 1`) gradient descent, one epoch means `N` updates since every individual data point is used to perform an update.
* For mini-batch of size `n`, one epoch has `N/n` updates since a mini-batch of `n` data points is used to perform an update.

# Restarting the process

Moving back to what we were doing before, now we use the updated parameters to go back to Step 1, and restart the process.

Repeating this process over and over for many epochs is training a model in a nutshell.

What happens if we run it over 1,000 epochs? We can check the results out by looking at the figure given below: 

Learn about what epochs are and how the path of a gradient descent can change depending on the type of gradient descent being used. 

Step 5 - Rinse and Repeat!

Learn about what epochs are and how the path of a gradient descent can change depending on the type of gradient descent being used.

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Step 5 - Rinse and Repeat!

Introduction to epoch

Definition

Updates and gradient descent