Introduction to Deep Learning

The University of Colorado Boulder, founded in 1876 and set against the Rocky Mountains, is a major research university serving more than 37,000 students with nearly 3,000 international learners. CU Boulder is recognized for academic excellence, expansive research programs and a sustainability-driven campus culture. Students benefit from an array of undergraduate, graduate and professional offerings that encourage innovation across science, engineering, business, arts and humanities.

Research, entrepreneurship and experiential learning are central to the CU Boulder experience, with extensive laboratory facilities, innovation labs and co-op or internship pathways that connect students to industry and civic partners. The university supports cross-disciplinary projects and provides robust advising, career services and student organizations that help translate academic interests into practical outcomes and start-up ventures.

Boulder's outdoor lifestyle and proximity to tech hubs create strong opportunities for networking, recreation and balanced living. International students are supported by dedicated offices for admissions, orientation and visa guidance, making the transition smoother. With a large program portfolio and a campus culture that values sustainability, creativity and collaboration, CU Boulder is well suited to students seeking high-impact research experiences and an active campus community.

The Introduction to Deep Learning program is a non-degree course that teaches students the basics of deep learning techniques. It's offered in English and lasts several weeks. The main advantage of this course is that it covers various types of data, including images, texts, and voice/sound.

The curriculum includes building and training multilayer perceptron, convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks. Students will develop skills in Python programming and apply their knowledge through hands-on projects, such as cancer detection with CNNs and generating dog images with GANs.

After completing this course, students can pursue careers as Machine Learning Engineers, Data Scientists, Computer Vision Engineers, or Natural Language Processing Specialists. They can work in industries like technology, healthcare, or finance, and be employed by companies like Facebook, Amazon, or NVIDIA.