Introduction to Deep Learning
MIT's introductory course on deep learning, covering neural network fundamentals and their application to computer vision, natural language processing, and biology. Lectures build up from the basics of algorithms and training methods to hands-on work building networks in TensorFlow. Assumes calculus and linear algebra background, with Python helpful but not required. The course concludes with a project proposal competition where students receive feedback from course staff and a panel of industry sponsors. Materials come from MIT's official course offering, part of its Electrical Engineering and Computer Science curriculum, and are freely available through MIT OpenCourseWare. Suited to learners wanting a structured, technical entry point into deep learning rather than a conceptual overview.