The payment is 100% refundable if you cancel before the first session, 50% if you withdraw before the second session, and 0% refund if withdraw after the second session. To request a refund, please contact [email protected]
The class is designed for people who have had little or no exposure to deep learning and deep neural networks and is designed to be an accelerated survey of the field. The course should be highly accessible for people with a broad range of backgrounds. No data science background is required. The course should be highly accessible for students with or without a degree in a technical discipline.
However, deep learning is not an entry-level subject. Ideally, students should have some familiarity with differential univariate and multivariate calculus, basic linear algebra and probability and statistics. We recognize that some or all of the mathematics may be rusty or long-forgotten. To this end the course will provide a math review covering the important mathematical tools relevant to support the topics covered. Students should also have basic skills in Python programming and be comfortable installing software frameworks within their chosen computing environment.