Introduction to Deep Learning

Massachusetts Institute of Technology via YouTube

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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.

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

Taught by

Alexander Amini

Reviews

4.8 rating, based on 24 Class Central reviews

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Cleiton Correa @CleitonCorreaEngineerWeb

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!
Kiruthika S

Deep Learning is an important part of Artificial Intelligence that focuses on training neural networks to learn from large amounts of data. This introduction provides a clear understanding of how machines can automatically extract features and make decisions similar to humans. The basic concepts like layers, neurons, and activation functions are well explained, making it easier for beginners to grasp. It also highlights real-world applications such as image recognition, speech processing, and self-driving systems. Overall, the introduction is informative and builds a strong foundation for further learning in deep learning and advanced AI topics.
Vishal Sharma

MIT’s Introduction to Deep Learning is an exceptional resource for anyone looking to transition from theory to practice. The course manages to demystify complex architectures—like Transformers and CNNs—through clear, high-energy lectures and excellent visual aids. What sets this apart from other MOOCs are the software labs; using TensorFlow to build models for facial recognition and music generation provided immediate, hands-on gratification. While it moves at a brisk 'MIT pace,' the foundational math is explained intuitively enough for intermediate learners. It’s easily one of the most polished and up-to-date deep learning courses available for free online."
Abuhani
1

Very good course but i still doesn't get my certificate kindly send it as soon as possible and i also refer it to my friends also but they also didn't get the certificate
Mohnish Sachdeva

The Deep Learning course was extremely informative and well-structured. It covered all major concepts including neural networks, CNNs, RNNs, optimization techniques, and practical model-building. The teaching style was clear, and the real-world examples helped in understanding complex topics easily.

Hands-on assignments and projects were the best part of the course—they allowed me to apply the concepts and gain confidence in building deep learning models. Overall, the course significantly improved my understanding of AI and deep learning, and I highly recommend it to anyone interested in this field.
Durga Darini SD

best explanation which helped me to learn easily actually i just started this course for certificate but after 2 classes i really enjoyed the learning
Renan Felix

That’s awesome! It might be a good idea to have an update at some point, since this segment evolves quite frequently.
18_kusuma Eega

It is very useful and easy to understand the topics in deep learning.so I am telling that it's very useful and important for getting jobs.
NADEEM NIZAMUDDIN SHAIKH

"The course was well-structured, covering both theory and practical applications of deep learning. It enhanced my understanding of neural networks and their real-world use cases."
Gaddapuwar Lokesh

Good explanation and give some hand Note will understand easily with good explanation and real life example will understand step by step in some doubts of are there in supper explain good 👍🏻
Thank you 🙏🏻
Pratyush Poddar

I recently completed the deep learning course and was thoroughly impressed by its comprehensive coverage and engaging presentation. The instructors broke down complex topics like neural networks, convolutional architectures, and reinforcement learni…

I recently completed the deep learning course and was thoroughly impressed by its comprehensive coverage and engaging presentation. The instructors broke down complex topics like neural networks, convolutional architectures, and reinforcement learning into digestible segments that were both accessible and informative. The balance between theory and hands-on projects allowed me to apply concepts in real-world scenarios, significantly enhancing my understanding. The course materials were well-organized, and the interactive assignments kept me motivated throughout the learning journey. Overall, it was a rewarding experience that deepened my knowledge in artificial intelligence and machine learning, and I would highly recommend it to anyone looking to advance their skills in this exciting field!
Devulapally Shirisha

This course is interesting and a very informative. This course is very useful to me.I have learnt more information regarding deep learning
Sopie Souz

I am very happy to learn about this particular topic and course this is a very helpful course and interesting I am have literally gained a lot of knowledge of deep learning by this specific course thanks for providing
Nirmitee Jojare

The 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…

The 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 learners to the core principles of neural networks, convolutional networks, recurrent networks, and more. The instructors skillfully explain complex topics in a clear and engaging manner, catering to both beginners and those with prior machine learning knowledge.

The course stands out for its hands-on approach, allowing students to apply theoretical knowledge to practical projects using popular frameworks like TensorFlow and PyTorch. The practical exercises and assignments enhance the understanding of key concepts and reinforce real-world application. Additionally, the inclusion of case studies and examples from diverse domains, such as computer vision and natural language processing, further enriches the learning experience.

One potential area for improvement could be the inclusion of more advanced topics to cater to learners seeking deeper insights. Overall, "Introduction to Deep Learning 2021" is a commendable course for anyone aspiring to grasp the foundations of deep learning, equipping them with valuable skills for the rapidly evolving field of artificial intelligence.
Saish Patil

MIT'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.
Abhinaya Cheluveru

Deep 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…

Deep 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 real-world datasets and practical projects was invaluable, allowing us to apply the theory effectively.

The instructors were highly knowledgeable, providing clear explanations and guidance throughout the course. The interactive nature of the course and the use of cutting-edge tools and frameworks made learning engaging and relevant. Additionally, the peer-to-peer learning environment fostered collaboration and problem-solving skills.

The only suggestion for improvement would be to provide more advanced topics for those who wish to delve even deeper into the field. Overall, this deep learning course has been an exceptional learning experience, equipping us with vital skills for the ever-evolving world of AI and machine learning.
Course@2504

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.
Oumayma

أعجبتني الدورة إنها مميزة من نوعها لم أشهد مثلها و إضافتا إلى هذا فهي مفيدة و جيدا أستطع التعلم من خلالها العديد من الأشياء الرائعة
Shreya Wadkar

course 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.
Abidah Noreen

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