Introduction to Deep Learning 2021

Massachusetts Institute of Technology via YouTube

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Details

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Provider

YouTube
Pricing

Free Online Course
Languages

English
Duration & workload

1 day 5 hours
Sessions

On-Demand

Found in

Overview

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.
MIT 6.S191: Convolutional Neural Networks.
MIT 6.S191: Deep Generative Modeling.
MIT 6.S191: Reinforcement Learning.
MIT 6.S191 | 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 10 Class Central reviews

Start your review of Introduction to Deep Learning 2021

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!
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.
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.
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.
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
Esmita Gupta

It was a great learning. Its a request to add some lab sessions of each algorithm or model for better understanding
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.
KOTA LAXMIKANTH

Good experience with great learning and teaching.I like the way of teaching.Very useful Content and helps me very much.
Devarlla Ajay Charan

Good to have this course. Now. Iam completely perfect on Deep learning in future it will be useful for me
Thankyou
Egbewale Damilola

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