Suggested preparation: Python, linear algebra and introductory machine learning.
About this course
Follow an applied introduction to neural networks for images and language. The course develops convolutional networks and transformers before discussing large language models and retrieval-augmented generation.
Topics and lesson examples
- 1: Introduction to Neural Networks and Deep Learning; Training Deep NNs
- 3: Deep Learning for Computer Vision – Building Convolutional Neural Networks from Scratch
- 6: Deep Learning for Natural Language – Embeddings
- 8: Deep Learning for Natural Language – Transformers, Self-Supervised Learning
- 11: Generative AI – Text-to-Image Models
Course at a glance
| Provider | MIT |
|---|---|
| Platform | YouTube · MIT OpenCourseWare |
| Access | Free Video |
| Level | Intermediate — WikiFree study-level assessment |
| Language | English |
| Available lessons | 11 |
| Video duration | 13h 45m · self-paced |
| Details checked | 10 October 2026 |
Preparation and study approach
Suggested preparation: Python, linear algebra and introductory machine learning.
Follow the available videos in sequence, pausing to summarize the main argument or reconstruct a worked example. Keep a short list of assumptions, unfamiliar terms and questions to revisit. Additional reading, problem solving and practice will take time beyond the listed video duration.
Using this video classroom
Select a lesson from the list, or search for a topic. Previous and Next move through the sequence. You can mark lessons complete; progress is stored in this browser without a WikiFree account.
Free access and course scope
The public MIT OpenCourseWare videos in this classroom are free to watch. This listing provides the available playlist recordings, which may include selected lectures, recitations, demonstrations or supporting discussions rather than every component of the original campus course. Textbooks, equipment, software licenses, supervised labs, graded assignments and instructor feedback are not supplied by WikiFree. No purchase is required to watch the videos or use the note-based study approach above.
WikiFree independently organizes these resources for self-paced study. Watching does not award an MIT certificate, academic credit or admission to MIT.
Video provider and credit: MIT OpenCourseWare on YouTube. WikiFree titles, descriptions and cover artwork are independently prepared.
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- Compare another approachFree Simplilearn Course: Deep Learning and Neural Networks
Focus: Neural Networks · CNNs
Intermediate - Compare another approachNeural Networks and Transformers: Free Course
Focus: Neural-network concepts · Gradient descent
Intermediate - Explore a higher-level optionFree MIT Course: Deep Learning Foundations and Neural Networks
Focus: Lec 09. Hacker's Guide to Deep Learning · Lec 17. Generalization: Out-of-Distribution (OOD)
Professional
Compare course focus, level and study time
| Course | Level | Focus | Study time |
|---|---|---|---|
| This course — Free MIT Course: Applied Deep Learning and Generative AI | Intermediate | 1: Introduction to Neural Networks and Deep Learning; Training Deep NNs · 3: Deep Learning for Computer Vision – Building Convolutional Neural Networks from Scratch · 6: Deep Learning for Natural Language – Embeddings | 13h 45m of video; practice time is additional |
| Free Simplilearn Course: Deep Learning and Neural Networks | Intermediate | Neural Networks · CNNs · RNNs | 78h 16m of video; practice time is additional |
| Neural Networks and Transformers: Free Course | Intermediate | Neural-network concepts · Gradient descent · Backpropagation | 3h 37m of video; practice time is additional |
| Free MIT Course: Deep Learning Foundations and Neural Networks | Professional | Lec 09. Hacker's Guide to Deep Learning · Lec 17. Generalization: Out-of-Distribution (OOD) · PyTorch Tutorial | 29h 31m of video; practice time is additional |
Provider: MIT. Videos play from the MIT OpenCourseWare channel on YouTube.
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