Congratulations, you've finished this course.
Overview
| Duration | 21 chapters |
| Difficulty |
|
For
- Domain experts with existing programming skills and mathematical knowledge
- CS students
Other Prerequisites
- Basic knowledge of linear algebra, calculus and probability
- Python programming skills
Outcomes
- Solid understanding of deep learning concepts
- Skills to apply deep learning methods
This interactive book provides comprehensive explanations, code and math for various deep learning topics. You will learn about fundamental concepts and techniques of different application areas, including Computer Vision and Natural Language Processing. The book includes clear explanations and detailed code snippets to help you understand the inner workings of deep learning models. All included examples are available in PyTorch, MXNet and TensorFlow, so you can continue to gain experience in the framework of your choice. By the end, you will have a broad knowledge to build your own models for a variety of applications while at the same time theoretically understanding the underlying concepts.
