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FreeMIT 15.773 Hands-On Deep Learning Spring 2024
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This MIT OpenCourseWare course is a brisk introduction to deep learning built around unstructured data. It covers deep neural networks and how to set them up and train them, convolutional networks for images and video, transformers for language, and generative large language models and text-to-image models.
The course expects Python and fundamental machine learning ideas: training and validation splits, overfitting and underfitting, and regularization. Learners without those will find the pace demanding.
The stated focus is practical understanding, building models for complex problems rather than only reading the theory. Pairing the lectures with your own small projects is the best way to turn the material into skill.
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15-773-hands-on-deep-learning-spring-2024
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60YyhMjYmXuVmX562QcClSp
This is a fast-paced introduction to Deep Learning with an emphasis on developing a practical understanding of how to build models to solve complex problems involving unstructured data. Topics include the basics of deep neural networks and how to set up and train them, convolutional networks to process images and videos, transformers for natural language processing, generative large language models (such as ChatGPT), and text-to-image models (such as MidJourney). Prior familiarity with Python and fundamental machine learning concepts (such as training/validation/testing, overfitting/underfitting, and regularization) is required.
License: Creative Commons BY-NC-SAMore information at https://ocw.mit.edu/termsMore courses at https://ocw.mit.eduSupport OCW at http://ow.ly/a1If50zVRlQ
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