Kaggle is a popular data-science website owned by Google. It started out with competitions in which participants had to build machine learning models in order to make predictions. However, over the years, it has also had a popular forum, an online learning system and, most importantly for us, a hosted Jupyter service.

To use Kaggle, you can visit their website at https://www.kaggle.com/. In order to use the site, you will be required to create an account.

After you've created your account, you can find the Kernels page by clicking on Kernels located in the main menu, as seen in the following screenshot:

Public Kaggle kernels

In the preceding screenshot, you can see a number of kernels that other people have both written and published. Kernels can be private, but publishing kernels is a good way to show skills and share knowledge.

To start a new kernel, click New Kernel. In the dialog that follows, you want to select Notebook:

The kernel editor

You will get to the kernel editor, which looks like the preceding screenshot.

Note that Kaggle is actively iterating on the kernel design, and so a few elements might be in different positions, but the basic functionality is the same. The most important piece of a notebook is the code cells. Here you can enter the code and run it by clicking the run button on the bottom left, or alternatively by pressing Shift + Enter.

The variables you define in one cell become environment variables, so you can access them in another cell. Markdown cells allow you to write text in markdown format to add a description to what is going on in your code. You can upload and download notebooks with the little cloud buttons featured in the top-right corner.

To publish a notebook from the kernel editor, firstly you must click the Commit & Run button and then set the notebook to Public in the settings. To enable a GPU on your notebook, make sure to check the Enable GPU button located in the bottom right. It's important to remember that this will restart your notebook, so your environment variables will be lost.

Once you run the code, the run button turns into a stop button. If your code ever gets stuck, you can interrupt it by clicking that stop button. If you want to wipe all environment variables and begin anew, simply click the restart button located in the bottom-right corner.

With this system, you can connect a kernel to any dataset hosted on Kaggle, or alternatively you can just upload a new dataset on the fly. The notebooks belonging to this book already come with the data connection.

Kaggle kernels come with the most frequently used packages preinstalled, so for most of the time you do not have to worry about installing packages.

Sometimes this book does use custom packages not installed in Kaggle by default. In this case, you can add custom packages at the bottom of the Settings menu. Instructions for installing custom packages will be provided when they are used in this book.

Kaggle kernels are free to use and can save you a lot of time and money, so it's recommended to run the code samples on Kaggle. To copy a notebook, go to the link provided at the beginning of the code section of each chapter and then click Fork Notebook. Note that Kaggle kernels can run for up to six hours.

$ jupyter notebook

$ sudo pip install TensorFlow

$ sudo pip install Keras