6.S191: Introduction to Deep Learning
MIT's official introductory deep learning course covering foundations, computer vision, sequential modeling, and generative AI.
- Duration
- 20+ hours
- Price
- Free
- Format
- Video
- Level
- Beginner
Free
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What you'll learn
- Understand perceptrons, gradient descent, and backpropagation
- Build convolutional networks for image classification and segmentation
- Explore generative architectures including VAEs, GANs, and diffusion models
- Train reinforcement learning agents for simulated environments
About this course
MIT's intensive course covering neural network foundations, convolutional networks, transformers, generative modeling, reinforcement learning, and AI bias. Taught by Alexander Amini and Ava Soleimany, featuring hands-on software labs in TensorFlow and PyTorch.
Prerequisites
Calculus and basic Python programming experience