Venelin Valkov - Get Sh*t Done with PyTorch

PyTorch is:

An open source machine learning framework that accelerates the path from research prototyping to production deployment.

In my humble opinion, PyTorch is the sweet way to solve Machine Learning problems, in the real world! The vast community allows you to work state-of-the-art models and deploy them to production in no time (relatively speaking). Lets get started!

Do you know NumPy? If you do, learning PyTorch will be a breeze! If you dont, prepare to learn the skills that will guide you on your journey Machine Learning Mastery!

Lets start with something simple:

Adding the same arrays with PyTorch looks like this:

Fortunately, you can go from NumPy to PyTorch:

and vice versa:

The good news is that the conversions incur almost no cost on the performance of your app. The NumPy and PyTorch store data in memory in the same way. That is, PyTorch is reusing the work done by NumPy.

Tensors are just n-dimensional number (including booleans) containers. You can find the complete list of supported data types at PyTorchs Tensor Docs.

So, how can you create a Tensor (try to ignore that Ive already shown you how to do it)?

You can create a tensor from floats:

Or define the type like so:

You can use a wide range of factory methods to create Tensors without manually specifying each number. For example, you can create a matrix with random numbers like this: