Introduction to Deep Learning

Is this course completely free to access?

Yes, all course materials are completely free and open-sourced to the world under MIT license. After the in-person program at MIT concludes each January, the lectures, slides, software labs, and code are made freely available online through the course website and YouTube channel. You can watch all lecture videos, download slides, and work through the programming labs without any cost. The only restriction is that if you’re an instructor using these materials, you must include proper attribution. For MIT students taking the course for credit, there are standard MIT tuition fees, but the educational content itself is accessible to everyone worldwide at no charge.

What prerequisites do I need before taking this course?

You need elementary knowledge of calculus (taking derivatives and applying the chain rule) and linear algebra (matrix multiplication). The course is designed to be beginner-friendly and aims to explain everything else along the way, since many registered students come from outside computer science. Experience in Python programming is helpful but not strictly necessary, as the course provides practical coding experience through the software labs. If you’re comfortable with basic mathematical operations and willing to learn Python as you go, you should be able to follow the intensive bootcamp format and complete the hands-on projects.

What topics are covered in this intensive program?

The one-week bootcamp covers comprehensive deep learning fundamentals including introduction to neural networks, deep sequence modeling and transformers, computer vision with convolutional networks, deep generative modeling, and deep reinforcement learning. Advanced topics include large language models and fine-tuning techniques, AI applications for scientific discovery in areas like materials design and drug discovery, and massively parallel training techniques for scaling to thousands of GPUs. The program also explores new frontiers in AI and ethical considerations. Each topic is accompanied by hands-on software labs where you build practical applications including music generation systems, facial detection with bias mitigation, and fine-tuned language models.

How is this intensive bootcamp format structured?

The course runs for one week every January (January 5-9 in 2026) with daily sessions from 1-4 PM ET at MIT. Each day includes lectures, software labs, and work time, creating an immersive learning experience. The fast-paced structure covers 10 lectures plus 3 hands-on labs over five days, concluding with a final project proposal competition. This intensive format is designed as a high-efficiency bootcamp that accelerates your learning compared to traditional semester-long courses. The compressed timeline requires dedicated focus during the week but allows you to gain comprehensive deep learning knowledge in just five days rather than several months.

How long does it take to complete this course?

This course takes about one to two weeks to complete if you’re studying intensively. The course has around 20 hours of video lectures plus hands-on labs. At MIT, it’s taught as a condensed one-week seminar during January, with daily sessions covering lectures and TensorFlow exercises. But if you’re learning on your own through the free online materials, you might want to spend a bit more time – maybe two weeks – to really absorb everything at a comfortable pace.

Who teaches this course and what makes it special?

The course is led by Alexander Amini and Ava Amini, experienced deep learning researchers and educators at MIT, with support from faculty sponsor Professor Daniela Rus and a team of teaching assistants. What makes this program unique is the combination of MIT’s academic rigor with cutting-edge industry perspectives through guest lectures from leaders at Microsoft, Liquid AI, and other top organizations. The intensive bootcamp format creates an immersive environment unlike traditional courses, and the inclusion of a project competition with industry sponsor feedback gives you real-world validation of your ideas. The course has been refined annually since 2017, with materials sponsored by tech giants including Google, IBM, NVIDIA, Microsoft, and Amazon, ensuring you’re learning the most relevant and practical deep learning skills.

What programming projects will I build during the labs?

You’ll complete three substantial software labs that provide hands-on experience with real deep learning applications. Lab 1 focuses on deep learning fundamentals in Python and building a music generation system using neural networks. Lab 2 involves creating facial detection systems while learning about algorithmic bias mitigation—an important ethical consideration in AI. Lab 3 teaches you to fine-tune large language models, giving you practical experience with the transformer models powering modern AI applications. All lab code is available on GitHub, allowing you to modify and extend the projects even after completing the course. These projects give you portfolio pieces demonstrating your ability to implement deep learning solutions to complex real-world problems.