This book is for aspiring data scientists and deep learning engineers who want to get started with the absolute fundamentals of deep learning and neural networks. Now, about requirements:
- No prior exposure to deep learning or machine learning is necessary, but it would be a plus.
- Some familiarity with linear algebra and Python programming is all you need to get started.
This book is for people who value their time and want to get to the point and learn the deep learning recipes needed to do things.
Deep learning can be intimidating if you don’t know the basics. Many people are discouraged because they cannot follow the terminology or sample programs they see on the web. This causes people to make poor decisions about the selection of deep learning algorithms and renders them unable to foresee the consequences of such choices. Therefore, this book is for people who do the following:
- Value access to good definitions of deep learning concepts
- Want a structured method to learn deep learning from scratch
- Desire to know the fundamental concepts and really understand them
- Want to know how to preprocess data for usage in deep learning algorithms
- Are curious about some advanced deep learning algorithms
For details about the contents of each chapter, read the next section.
