Run the Jupyter notebooks on your machine

To read or run these documents on your machine, you should first install Jupyter Notebook. For those who already use Anaconda (https://www.anaconda.com) to manage and deploy their Python environments (as we will recommend in this book), Jupyter Notebook should be directly available (as it is installed with Anaconda). For those using other Python distributions and those not familiar with Jupyter Notebook, we recommend having a look at the documentation, which provides installation instructions and tutorials (https://jupyter.org/documentation).

Once Jupyter Notebook is installed on your machine, navigate to the directory containing the book's code files, open a terminal, and execute the following command:

$ jupyter notebook

The web interface should open in your default browser. From there, you should be able to navigate the directory and open the Jupyter notebooks provided, either to read, execute, or edit them.

Some documents contain advanced experiments that can be extremely compute-intensive (such as the training of recognition algorithms over large datasets). Without the proper acceleration hardware (that is, without compatible NVIDIA GPUs, as explained in Chapter 2, TensorFlow Basics and Training a Model), these scripts can take hours or even days (even with compatible GPUs, the most advanced examples can take quite some time).