Let’s take a moment to look at the technical tools required for this course.

In this section, we’ll be introduced to the technical tools that will be used in the exercises of the following chapters. First, we’ll present a brief introduction to the main tools provided. Next, we’ll present a rough guide on how to install each tool, along with hyperlinks to detailed guides provided by the official websites. Additionally, we’ll share tips on how to make sure that the tools are installed properly.

Description of the tools

We’ll 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 and machine learning communities.

Additionally, there are numerous scientific libraries built for 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’ll use Jupyter Notebooks in all our exercises because it provides a rich and interactive environment for coding compared to using Python scripts. We’ll also use pandas, NumPy, and scikit-learn—three popular libraries for Python—for various purposes, such as data preprocessing. Another library we will be using for various text-related operations is NLTK—the Python Natural Language Toolkit. Finally, we will use Matplotlib for data visualization.

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