Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models.

We recently launched one of the first online interactive deep learning course using Keras 2.0, called "Deep Learning in Python".

Now, DataCamp has created a Keras cheat sheet for those who have already taken the course and that still want a handy one-page reference or for those who need an extra push to get started.

In no time, this Keras cheat sheet will make you familiar with how you can load datasets from the library itself, preprocess the data, build up a model architecture, and compile, train, and evaluate it. As there is a considerable amount of freedom in how you build up your models, you'll see that the cheat sheet uses some of the simple key code examples of the Keras library that you need to know to get started with building your own neural networks in Python.

Furthermore, you'll also see some examples of how to inspect your model, and how you can save and reload it. Lastly, you’ll also find examples of how you can predict values for test data and how you can fine tune your models by adjusting the optimization parameters and early stopping.

In short, you'll see that this cheat sheet not only presents you with the six steps that you can go through to make neural networks in Python with the Keras library.

In short, this cheat sheat will boost your journey with deep learning in Python: you'll have preprocessed, created, validated and tuned your deep learning models in no time thanks to the code examples!

(Click above to download a printable version or read the online version below.)