# Let’s get started

Welcome to LearningTensorFlow.com! Our aim is to teach TensorFlow from the ground up, without requiring you to know or learn, deep learning at the same time. We found many tutorials were too heavy on the machine learning parts, and skipped over important details of how it is implemented in TensorFlow, making it difficult to start creating your own programs.

Choose your own adventure, by selecting which set of TensorFlow tutorials you’d like to start with. If you are new to TensorFlow, we recommend you start with Basics. If you have some experience, check out Learning or Distributing.

That all said, if you want a roadmap on where to go, follow the path set below.

### You'll need TensorFlow Installed!

It might sound obvious, but you'll need to have TensorFlow installed before you can do anything useful with our lessons. Installing TensorFlow is not trivial, but Google have released good instructions for getting started.

Use the official TensorFlow instructions here to get started.

Be sure to select whether you want to use your GPU or not - you'll need it in a later tutorial, but if you don't have a supported GPU, that's OK.

# Course 1: TensorFlow Basics

Learn the basics of TensorFlow, including variables, constants and operations. There are the building blocks for larger programs in TensorFlow, and with this information you can start creating basic programs to do some math!

Note that our tutorials assume that you have some Python experience already. Check out our resources page if you do not, and we’ll be here when you get back.

# Course 2: Linear Algebra

Underlying everything TensorFlow does is linear algebra. This set of lessons goes through the underlying concepts of linear algebra, matrices and… tensors! These concepts are core to the later work you’ll want to do with TensorFlow and even a passing knowledge can get you quite far.

We do cover a little math, but do not assume that you come in with any working knowledge of matrices or other linear algebra.

# Course 3: Machine Learning

Once you know the basics of matrices and tensors, you’ll probably want to be creating machine learning models with them.
This course goes into the *concepts* of machine learning, the basic algorithms and concepts you’ll need later, as well as touching on actual implementation details.

Prior machine learning knowledge is not assumed.

# Course 4: Distributed Processing

Once your machine learning models start getting bigger, you’ll need bigger computers. TensorFlow has tools and techniques for distributing computation to many computers and other options. This set of tutorials takes you through the basics of how to distribute the workload when you have big data, and cannot work on it locally.

These tutorials assume that you’ve done the previous courses, but no other knowledge is required.