MIT’s introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow.
MIT Introduction to Deep Learning 6.S191: Lecture 1
MIT 6.S191: Introduction to Deep Learning
Foundations of Deep Learning
Lecturer: Alexander Amini
January 2019
MIT Introduction to Deep Learning 6.S191: Lecture 2
MIT 6.S191: Recurrent Neural Networks
Deep Sequence Modeling with Recurrent Neural Networks
Lecturer: Ava Soleimany
January 2019
MIT Introduction to Deep Learning 6.S191: Lecture 3
MIT 6.S191: Convolutional Neural Networks
Deep Computer Vision
Lecturer: Ava Soleimany
January 2019
MIT Introduction to Deep Learning 6.S191: Lecture 4
MIT 6.S191: Deep Generative Modeling
Deep Generative Modeling
Lecturer: Alexander Amini
January 2019
MIT Introduction to Deep Learning 6.S191: Lecture 5
MIT 6.S191: Deep Reinforcement Learning
Deep Reinforcement Learning
Lecturer: Alexander Amini
January 2019
MIT Introduction to Deep Learning 6.S191: Lecture 6
MIT 6.S191: Deep Learning Limitations and New Frontiers
Deep Learning Limitations and New Frontiers
Lecturer: Ava Soleimany
January 2019
MIT Introduction to Deep Learning 6.S191: Lecture 7
MIT 6.S191: Visualization for Machine Learning (Google Brain)
Data Visualization for Machine Learning
Lecturer: Fernanda Viegas
Google Brain Guest Lecture
January 2019
MIT Introduction to Deep Learning 6.S191: Lecture 8
MIT 6.S191: Biologically Inspired Neural Networks (IBM)
A Biologically Plausible Learning Algorithm for Neural Networks
Lecturer: Dmitry Krotov
MIT/IBM Watson AI Lab Guest Lecture
January 2019
MIT Introduction to Deep Learning 6.S191: Lecture 9
MIT 6.S191: Image Domain Transfer (NVIDIA)
Learning and Perception: Image Domain Transfer
Lecturer: Jan Kautz
NVIDIA Guest Lecture
January 2019
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