This is an online version of the Manning book Inside Deep Learning: Math, Algorithms, Models MEAP V01 epub. With liveBook you can access Manning books in-browser — anytime, anywhere.
I’m delighted that you’ve purchased the MEAP for my book Inside Deep Learning: Math, Algorithms, Models. This book is geared toward those with a firm programing background who have some machine learning (ML) experience but want to go deeper. You are in good shape to reach through this book if you are comfortable with the basics of calculus, linear algebra, and statistics that go into machine learning.
If you can record the inputs and correct outputs for a task, deep learning can help you translate that task from human time and effort to an automated process. The input could be an image, and the output “cat”, or “dog” for example, would describe the images’ content. The input could be an English sentence, and the output a French sentence with the same meaning. That’s machine translation. The input could be the description “cat” and the output be an actual image of a cat! This input/output nature makes deep learning widely applicable to almost any domain you can think of. It's why I got into machine learning in the first place, giving me a chance to have an impact and contribute to solving real problems in almost any domain.
Deep learning (also called neural networks) gives us tools to help improve the quality of life in big and small ways, and my sincere desire is to pass that along to you. By the end of this book, you should understand:
- What deep learning is
- How to build and modify deep learning models
- Which “building blocks” you should look toward for a given problem