Deep Learning can help you create high-quality and highly realistic videos and quality models for generating those videos. It can be used to create fully simulated environments of the real world and create virtual worlds.Deep Learning is subset of machine learning focused on extracting patterns from data using neural networks and use those patterns to inform the learning tasks. It is all about teaching computers how to learn a task from raw data.The course will start with the foundations of deep learning and neural networks and conclude with guest lectures and student projects.
Overview
Syllabus
MIT Introduction to Deep Learning | 6.S191
MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention
MIT 6.S191: Convolutional Neural Networks
MIT 6.S191: Deep Generative Modeling
MIT 6.S191: Reinforcement Learning
MIT 6.S191: Language Models and New Frontiers
MIT 6.S191: (Google) Generative AI for Media
MIT 6.S191: Building AI Models in the Wild
MIT Introduction to Deep Learning (2023) | 6.S191
MIT 6.S191 (2023): Recurrent Neural Networks, Transformers, and Attention
MIT 6.S191 (2023): Convolutional Neural Networks
MIT 6.S191 (2023): Deep Generative Modeling
MIT 6.S191 (2023): Robust and Trustworthy Deep Learning
MIT 6.S191 (2023): Reinforcement Learning
MIT 6.S191 (2023): Deep Learning New Frontiers
MIT 6.S191 (2023): Text-to-Image Generation
MIT 6.S191 (2023): The Modern Era of Statistics
MIT 6.S191 (2023): The Future of Robot Learning
MIT Introduction to Deep Learning (2022) | 6.S191
MIT 6.S191 (2022): Recurrent Neural Networks and Transformers
MIT 6.S191 (2022): Convolutional Neural Networks
MIT 6.S191 (2022): Deep Generative Modeling
MIT 6.S191 (2022): Reinforcement Learning
MIT 6.S191 (2022): Deep Learning New Frontiers
MIT 6.S191: LiDAR for Autonomous Driving
MIT 6.S191: Automatic Speech Recognition
MIT 6.S191: AI for Science
MIT 6.S191: Uncertainty in Deep Learning
MIT 6.S191 (2021): Introduction to Deep Learning
MIT 6.S191 (2021): Recurrent Neural Networks
MIT 6.S191 (2021): Convolutional Neural Networks
MIT 6.S191 (2021): Deep Generative Modeling
MIT 6.S191 (2021): Reinforcement Learning
MIT 6.S191 (2021): Deep Learning New Frontiers
MIT 6.S191: Evidential Deep Learning and Uncertainty
MIT 6.S191: AI Bias and Fairness
MIT 6.S191: Deep CPCFG for Information Extraction
MIT 6.S191: Taming Dataset Bias via Domain Adaptation
MIT 6.S191: Towards AI for 3D Content Creation
MIT 6.S191: AI in Healthcare
MIT 6.S191 (2020): Introduction to Deep Learning
MIT 6.S191 (2020): Recurrent Neural Networks
MIT 6.S191 (2020): Convolutional Neural Networks
MIT 6.S191 (2020): Deep Generative Modeling
MIT 6.S191 (2020): Reinforcement Learning
MIT 6.S191 (2020): Deep Learning New Frontiers
MIT 6.S191 (2020): Neurosymbolic AI
MIT 6.S191 (2020): Generalizable Autonomy for Robot Manipulation
MIT 6.S191 (2020): Neural Rendering
MIT 6.S191 (2020): Machine Learning for Scent
Barack Obama: Intro to Deep Learning | MIT 6.S191
MIT 6.S191 (2019): Introduction to Deep Learning
MIT 6.S191 (2019): Recurrent Neural Networks
MIT 6.S191 (2019): Convolutional Neural Networks
MIT 6.S191 (2019): Deep Generative Modeling
MIT 6.S191 (2019): Deep Reinforcement Learning
MIT 6.S191 (2019): Deep Learning Limitations and New Frontiers
MIT 6.S191 (2019): Visualization for Machine Learning (Google Brain)
MIT 6.S191 (2019): Biologically Inspired Neural Networks (IBM)
MIT 6.S191 (2019): Image Domain Transfer (NVIDIA)
MIT 6.S191 (2018): Introduction to Deep Learning
MIT 6.S191 (2018): Sequence Modeling with Neural Networks
MIT 6.S191 (2018): Convolutional Neural Networks
MIT 6.S191 (2018): Deep Generative Modeling
MIT 6.S191 (2018): Deep Reinforcement Learning
MIT 6.S191 (2018): Deep Learning Limitations and New Frontiers
MIT 6.S191 (2018): Issues in Image Classification
MIT 6.S191 (2018): Faster ML Development with TensorFlow
MIT 6.S191 (2018): Deep Learning - A Personal Perspective
MIT 6.S191 (2018): Beyond Deep Learning: Learning+Reasoning
MIT 6.S191 (2018): Computer Vision Meets Social Networks
MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention
MIT 6.S191: Convolutional Neural Networks
MIT 6.S191: Deep Generative Modeling
MIT 6.S191: Reinforcement Learning
MIT 6.S191: Language Models and New Frontiers
MIT 6.S191: (Google) Generative AI for Media
MIT 6.S191: Building AI Models in the Wild
MIT Introduction to Deep Learning (2023) | 6.S191
MIT 6.S191 (2023): Recurrent Neural Networks, Transformers, and Attention
MIT 6.S191 (2023): Convolutional Neural Networks
MIT 6.S191 (2023): Deep Generative Modeling
MIT 6.S191 (2023): Robust and Trustworthy Deep Learning
MIT 6.S191 (2023): Reinforcement Learning
MIT 6.S191 (2023): Deep Learning New Frontiers
MIT 6.S191 (2023): Text-to-Image Generation
MIT 6.S191 (2023): The Modern Era of Statistics
MIT 6.S191 (2023): The Future of Robot Learning
MIT Introduction to Deep Learning (2022) | 6.S191
MIT 6.S191 (2022): Recurrent Neural Networks and Transformers
MIT 6.S191 (2022): Convolutional Neural Networks
MIT 6.S191 (2022): Deep Generative Modeling
MIT 6.S191 (2022): Reinforcement Learning
MIT 6.S191 (2022): Deep Learning New Frontiers
MIT 6.S191: LiDAR for Autonomous Driving
MIT 6.S191: Automatic Speech Recognition
MIT 6.S191: AI for Science
MIT 6.S191: Uncertainty in Deep Learning
MIT 6.S191 (2021): Introduction to Deep Learning
MIT 6.S191 (2021): Recurrent Neural Networks
MIT 6.S191 (2021): Convolutional Neural Networks
MIT 6.S191 (2021): Deep Generative Modeling
MIT 6.S191 (2021): Reinforcement Learning
MIT 6.S191 (2021): Deep Learning New Frontiers
MIT 6.S191: Evidential Deep Learning and Uncertainty
MIT 6.S191: AI Bias and Fairness
MIT 6.S191: Deep CPCFG for Information Extraction
MIT 6.S191: Taming Dataset Bias via Domain Adaptation
MIT 6.S191: Towards AI for 3D Content Creation
MIT 6.S191: AI in Healthcare
MIT 6.S191 (2020): Introduction to Deep Learning
MIT 6.S191 (2020): Recurrent Neural Networks
MIT 6.S191 (2020): Convolutional Neural Networks
MIT 6.S191 (2020): Deep Generative Modeling
MIT 6.S191 (2020): Reinforcement Learning
MIT 6.S191 (2020): Deep Learning New Frontiers
MIT 6.S191 (2020): Neurosymbolic AI
MIT 6.S191 (2020): Generalizable Autonomy for Robot Manipulation
MIT 6.S191 (2020): Neural Rendering
MIT 6.S191 (2020): Machine Learning for Scent
Barack Obama: Intro to Deep Learning | MIT 6.S191
MIT 6.S191 (2019): Introduction to Deep Learning
MIT 6.S191 (2019): Recurrent Neural Networks
MIT 6.S191 (2019): Convolutional Neural Networks
MIT 6.S191 (2019): Deep Generative Modeling
MIT 6.S191 (2019): Deep Reinforcement Learning
MIT 6.S191 (2019): Deep Learning Limitations and New Frontiers
MIT 6.S191 (2019): Visualization for Machine Learning (Google Brain)
MIT 6.S191 (2019): Biologically Inspired Neural Networks (IBM)
MIT 6.S191 (2019): Image Domain Transfer (NVIDIA)
MIT 6.S191 (2018): Introduction to Deep Learning
MIT 6.S191 (2018): Sequence Modeling with Neural Networks
MIT 6.S191 (2018): Convolutional Neural Networks
MIT 6.S191 (2018): Deep Generative Modeling
MIT 6.S191 (2018): Deep Reinforcement Learning
MIT 6.S191 (2018): Deep Learning Limitations and New Frontiers
MIT 6.S191 (2018): Issues in Image Classification
MIT 6.S191 (2018): Faster ML Development with TensorFlow
MIT 6.S191 (2018): Deep Learning - A Personal Perspective
MIT 6.S191 (2018): Beyond Deep Learning: Learning+Reasoning
MIT 6.S191 (2018): Computer Vision Meets Social Networks
Taught by
Alexander Amini
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Reviews
4.8 rating, based on 13 Class Central reviews
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Extremely professional and excellent course. Couldn't expect less from MIT. Congratulations, and may more courses like this come forward. Thank you immensely and I don't know how to thank you for this initiative. Thank you very much. Really a great course and I highly recommend it!
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Saish PatilMIT's "Introduction to Deep Learning 2021" on YouTube, taught by Alexander Amini and Ava Soleimany, offers a comprehensive introduction to deep learning. The course covers foundational concepts, training techniques, architectures like CNNs and RNNs, and advanced topics such as transformers and self-supervised learning. Strengths include expert instructors, extensive coverage, and practical coding sessions using TensorFlow and PyTorch. However, the course's fast pace, assumed prerequisites in math and programming, and lack of interactivity due to the YouTube format may challenge some learners. Overall, it's a valuable resource for those seeking in-depth knowledge of deep learning. -
Nirmitee JojareThe course "Introduction to Deep Learning 2021" provides a comprehensive and accessible overview of the fundamental concepts and techniques in the field of deep learning. Through a well-structured curriculum, the course effectively introduces learne… -
I have already completed my final year project in Deep Learning, and I wanted to further explore this field, which is why I chose to take this course for additional knowledge. The course has helped me to learn more about Deep Learning, and the videos are well structured and easy to understand. Thank you.
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I learn so much .In the course learn important points. It is very important course. It is important course in the future and placements. It is used for coding.it is used in the future. It is very easy and simple course but there are very important are there.Everyone learn this course
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Oumaymaأعجبتني الدورة إنها مميزة من نوعها لم أشهد مثلها و إضافتا إلى هذا فهي مفيدة و جيدا أستطع التعلم من خلالها العديد من الأشياء الرائعة -
Abhinaya CheluveruDeep learning has been an incredibly insightful and transformative journey. The course content was rich, offering a comprehensive understanding of complex neural networks, convolutional networks, and recurrent networks. The hands-on experience with… -
Shreya Wadkarcourse was quite informative.The course was well-structured, with each module building on the previous one. The gradual progression made it easy to grasp even for beginners.
The course materials, including lecture notes, video lectures, and supplementary reading, were of high quality.This course provided a comprehensive overview of deep learning, covering everything from neural networks and backpropagation to convolutional and recurrent neural networks. It even delved into cutting-edge topics like generative adversarial networks and reinforcement learning. -
i am abidah from pakistan and doing job in a organization which works on education .deep learning is an emerging feild and i have learnt much more from this course
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Good experience with great learning and teaching.I like the way of teaching.Very useful Content and helps me very much.
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Good to have this course. Now. Iam completely perfect on Deep learning in future it will be useful for me
Thankyou -
It was a great learning. Its a request to add some lab sessions of each algorithm or model for better understanding
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Deep learning by instructor Alexander Amini taught artificial intelligence through the use of deep learning the introductory part is using a single image or multiple image to get one goal or a particular thing
