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