Introduction to Deep Learning with PyTorch
Chapter 9: Summary
Deep Learning with PyTorch - Summary
Aaaaaaaaaaand… end of the lesson!
Let’s recap!
In this lesson we covered:
- How Gradient Descent works and how it can be implemented.
- How to create and play with
PyTorchtensors. - How to perform automatic optimisation of parameters using
PyTorch
We also provided a quick introduction to Deep Learning, and put into practice some of its concepts in two examples.
Are you able to answer all the questions below?
- Do you know how to implement Gradient Descent?
- Do you know how to automatically optimise some parameters to minimise a loss using
PyTorch? - How to create a
PyTorchtensor? - How to reshape and concatenate
PyTorchtensors? - How to implement a linear regression model?
- How to train a linear regression model?
- How to implement and train a classification model?
- How to implement and train an autoencoder?
That’s quite a lot covered in just a few chapters.
Don’t forget that all content of this lesson is non-examinable.
But hopefully, it will be useful for your incoming deep learning tutorials! ;)