Deep Learning with TensorFlow & Keras

Yes, our courses are designed to accommodate learners with varying levels of experience. All that is required is a basic understanding of at least one programming language (Python is preferable but not mandatory). We will walk you through the fundamental concepts, providing step-by-step guidance.

To get the most from our courses, you should possess a working knowledge of Python or a similar programming language. For the courses offered in C++, you should have a basic proficiency in C++.

Aside from the programming experience mentioned above, the series of courses are designed to take you from the fundamentals in Image Processing and Computer Vision through more advanced topics in Deep Learning. If you are looking to jump in directly to our Deep Learning courses, then you should have a good understanding of the foundational material in Image Processing and Computer Vision.

Upon finishing a course, you will be awarded a certificate of completion from OpenCV.org. To qualify for the certificate, you must complete all graded quizzes, assignments, and projects, obtaining a score of at least 50% within six months of enrollment. If your score exceeds 70%, you will be granted an Honor Certificate.

Our team will be available in the course forum to address your questions and provide guidance as needed. We encourage students to post questions on the forum, as other students can often help. In fact, you will find that answering questions for other students will facilitate your own learning. However, rest assured that our instructors monitor the course forum, and we strive to respond within 24 hours. In the end, it’s a highly collaborative environment that allows everyone involved the opportunity to learn.

The course content features a combination of theoretical explanations and practical code demonstrations delivered through both text and video formats. Quizzes, assignments, and projects of varying difficulty levels are also included. Students have the option to choose the assignments or projects they wish to work on, with each carrying a point value. We select the best "m" out of "n" assignments/projects to accommodate individual interests.

This approach allows students to focus on topics that pique their interest, enabling them to delve deeper by completing relevant assignments/projects while having the flexibility to skip topics they find less appealing.