Learn and practice PyTorch, a framework for developing deep learning models.

Become a Deep Learning Professional

In this module, we’ll be introduced to the fundamentals of PyTorch: autograd, model classes, datasets, data loaders, and more. We’ll develop models and understand them step-by-step. We'll understand the reasoning behind the code and how to avoid some common pitfalls and errors along the way.
By the time we finish this module, we’ll have a thorough understanding of the concepts and tools necessary to start developing and training our own models using PyTorch.

Deep Learning with PyTorch

## Nested models

In our model, we manually created two parameters to perform a linear regression. Instead of defining individual parameters, what if we use PyTorch’s [`Linear`](https://bit.ly/2Ezu181) model? 

We are implementing a single feature linear regression, one input, and one output, so the corresponding linear model would look like this:

import torch.nn as nn

linear = nn.Linear(1, 1)
print(linear)

codesnip1

Do we still have our `b` and `w` parameters? Sure we do!

import torch.nn as nn

linear = nn.Linear(1, 1)
print(linear.state_dict())

So, our former parameter `b` is the bias, and our former parameter `w` is the weight. Your values will be different since random seed has not been set up for this example.

Now, let us use PyTorch’s `Linear` model as an attribute of our own, thus creating a nested model.

# Nested models

In our model, we manually created two parameters to perform a linear regression. Instead of defining individual parameters, what if we use PyTorch’s [`Linear`](https://bit.ly/2Ezu181) model? 

We are implementing a single feature linear regression, one input, and one output, so the corresponding linear model would look like this:

Learn about the different types of models that are present in Deep Learning. 

Model Types

Learn about the different types of models that are present in Deep Learning.

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Appendix

Conclusion

Model Types

Nested models