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Massachusetts Institute of Technology

MIT 6.S191: Introduction to Deep Learning 2021

Massachusetts Institute of Technology via YouTube

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

5.0 rating, based on 2 Class Central reviews

Start your review of MIT 6.S191: Introduction to Deep Learning 2021

  • 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!
  • 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

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