It is expected that most of you have had prior experience with implementing machine learning models and have at least a basic understanding of how they work. It is also assumed that many of you have some prior experience with calculus, linear algebra, probability, and statistics; having this prior experience will help you get the most out of this book.
For those of you who do have prior experience with the mathematics covered in the first five chapters and have a background in machine learning, you are welcome to skip ahead to the content from Chapter 7, Feedforward Neural Networks, onward and keep with the flow of the book from there.
However, for the reader who lacks the aforementioned experience, it is recommended that you stay with the flow and order of the book and pay particular attention to understanding the concepts covered in the first five chapters, moving on to the next chapter or section only when you feel comfortable with what you have learned. It is important that you do not rush or be hasty, as DL is a vast and complex field that should not be taken lightly.
Lastly, to become a very good DL practitioner, it is important that you keep learning and going over the fundamental concepts, as these can often be forgotten quite easily. After having gone through all the chapters in the book and through all the chapters, I recommend trying to read the code for and/or implementing a few architectures and trying to recall what you have learned in this book because doing so will help ground your concepts even further and help you to learn much faster.
