Welcome aboard! In this lesson, we'll embark on the fascinating journey of neural networks and deep learning employing TensorFlow, an open-source library developed by the Google Brain team. We'll grasp its essence then delve into the Digits dataset from scikit-learn.

TensorFlow offers a flexible, efficient, and extensible framework for an array of machine learning and deep learning models. Such models find applications in numerous areas like image and speech recognition and the generation of art and music. Essentially, TensorFlow forms the backbone of deep artificial learning, enabling us to develop, train, and evaluate fundamental deep learning models.

On the other hand, the Digits dataset is part of scikit-learn, a renowned Python library for machine learning. Containing 8x8 images of digits, the Digits dataset aims to aid us in building and training our neural network models, particularly for pattern recognition.

In this course we'll learn how we can train a neural network that's able to recognize handwritten images, cool huh? Let's get to it!

Having unraveled what TensorFlow and the Digits dataset are, we'll start by importing these for our use. In Python, the import keyword brings various libraries into our projects.

First, let's import TensorFlow and alias it as tf:

To import the Digits dataset, which resides in the datasets module of the scikit-learn library, we can use: