Introduction to Deep Learning
6.S191 · Electrical Engineering and Computer Science · Undergraduate · January IAP 2020
Alexander Amini, Ava Soleimany
This is MIT’s introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication…
The syllabus, on MIT OpenCourseWare
The full course — syllabus, assigned readings, problem sets, exams, and lecture notes — lives on OCW. These open the real thing:
Syllabus ↗
Course overview, grading, schedule
Readings ↗
The assigned reading list, session by session
Assignments ↗
Problem sets and projects
Full course on OCW ↗
Everything, including lecture materials
Attribution
Alexander Amini, Ava Soleimany. 6.S191 Introduction to Deep Learning. January IAP 2020. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: CC BY-NC-SA 4.0.
Course materials are © their authors and licensed CC BY-NC-SA 4.0. CurrMana links to the source and does not re-host them.