To get the most out of this book
It would be very helpful to have a fundamental understanding of, and experience with, the Keras API, as this book pivots on a TensorFlow version beyond 2.x, in which the Keras API is officially supported and adopted as the tf.keras API. In addition, having a basic understanding of image classification techniques (convolution, and multiclass classification) would be helpful, as this book reuses the image classification problem as a vehicle to introduce and explain new features in TensorFlow Enterprise 2. Another helpful tool is GitHub. Basic experience with cloning GitHub repositories and navigating file structures would be very helpful for downloading the source code in this book.
From the ML perspective, having a basic understanding of model architectures, feature engineering processes, and hyperparameter optimization would be helpful. It is also assumed that you are familiar with fundamental Python data structures, including NumPy arrays, tuples, and dictionaries.
If you are using the digital version of this book, we advise you to type the code in yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to the copying/pasting of code.
