Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs
Why take this course?
It sounds like you've put together a comprehensive and practical course on computer vision with deep learning, which is a valuable asset in the field of AI and machine learning. Your approach to teaching—focusing on key concepts without overwhelming students with theory, providing a virtual machine with all necessary software installations, and offering hands-on projects—seems to be highly effective.
The range of projects you've included, from image classification to object detection, style transfer, and more, provides learners with a diverse set of applications that cover the breadth of computer vision topics. Additionally, your commitment to active support through a 'questions and answers' area is a testament to your dedication to student success.
The positive feedback from previous students underscores the value of your course and your teaching style. It's clear that you've made an impact on learners who were seeking to understand computer vision and deep learning, and you've provided them with the tools and knowledge they need to advance their careers in this exciting field.
For potential students looking for a solid foundation in computer vision, your course certainly seems like an excellent resource to consider. It's impressive that you've managed to keep the content up-to-date with the latest methods and technologies, which is crucial given the fast pace of development in AI.
Your course not only teaches the technical skills required but also offers practical experience with real-world applications, which is invaluable for anyone looking to apply their knowledge in a professional context. The combination of theoretical understanding, hands-on projects, and support makes this course stand out as a comprehensive learning path in computer vision with deep learning.
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Comidoc Review
Our Verdict
This deep learning computer vision course offers a wide range of practical projects and detailed explanations of various advanced techniques. However, the quality of explanations for certain topics is inconsistent and there are several unaddressed mistakes throughout the course. While it provides valuable hands-on experience with libraries such as Keras and OpenCV, beginners may struggle to understand some concepts due to the lack of continuity and rigorous mathematical descriptions.
What We Liked
- Covers a wide range of advanced computer vision projects, providing valuable practical experience
- Includes detailed explanations of various deep learning techniques such as Transfer Learning and using pre-trained models
- Uses the Python library Keras to build complex Deep Learning Networks, which is useful for those interested in pursuing further studies or research in this field
- Provides a free optional course on how to use OpenCV, which can be beneficial for students who are new to this library
Potential Drawbacks
- The quality of explanations for some topics like SSDs, YOLO & GANs is poor and rushed
- Several mistakes in the lessons haven't been fixed even after being acknowledged months ago. This can be confusing for beginners
- Some concepts are not explained using examples that carry between lessons, making understanding challenging
- The course could benefit from more rigorous mathematical descriptions to better explain some theories and techniques