We will use Python as the coding/scripting language. Python is a very versatile easy-to-set-up coding language that is heavily used by the scientific community. Additionally, there are numerous scientific libraries floating around Python, catering to areas ranging from deep learning to probabilistic inference to data visualization. TensorFlow is one such library that is well-known among the deep learning community, providing many basic and advanced operations that are useful for deep learning. Next, we will use Jupyter notebooks in all our exercises as it provides a more interactive environment for coding compared to using an IDE. We will also use scikit-learn—another popular machine learning toolkit for Python—for various miscellaneous purposes such as data preprocessing. Another library we will be using for various text related operations is NLTK—Python natural language toolkit. Finally, we will use matplotlib for data visualization.

Python is hassle-free to install in any of the commonly used operating systems such as Windows, macOS, or Linux. We will use Anaconda to set up Python, as it does all the laborious work for setting up Python as well as the essential libraries.

To install Anaconda, follow these steps:

To check whether Anaconda was properly installed, follow these steps:

If installed properly, the version of the current Anaconda distribution should be shown in Terminalthe instructions at http://scikit-learn.org/stable/install.html, NLTK from https://www.nltk.org/install.html and Matplotlib from https://matplotlib.org/users/installing.html.

You can install Jupyter Notebook by following the instruction at http://jupyter.readthedocs.io/en/latest/install.html.

To check whether Jupyter Notebook is properly installed, follow these steps:

Follow the instructions given at https://www.tensorflow.org/install/ under the Installing with Anaconda subsection to install TensorFlow. We will use TensorFlow 1.8.x throughout all the exercises.

When providing the tfBinaryURL as asked in the instruction, make sure that you provide a TensorFlow 1.8.x version. We stress this as the API has undergone many changes compared to the previous TensorFlow versions.

To check whether TensorFlow installed properly, follow these steps:

If all went well, you should not have any errors (there might be warnings if your computer does not have a dedicated GPU, but you can ignore them) and the TensorFlow version 1.8.x should be shown.