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6.S898 Deep Learning, Fall 2022

phillipi.github.io · 2,133 words · saved by 1 readers

Description: Fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, transformers), backpropagation and automatic differentiation, learning theory and generalization in high-dimensions, and applications to computer vision, natural language processing, and robotics. Pre-requisites: (6.3900 [6.036] or 6.C01 or 6.3720 [6.401]) and (6.3700[6.041] or 6.3800 [6.008] or 18.05) and (18.C06 or 18.06) Note: This is course is appropriate for advanced undergraduates and graduate students, and is 3-0-9 units. For non-students who want access to Piazza or Canvas, email Aidan Curtis (curtisa@mit.edu) to be added manually. For non-MIT students, refer to cross-registeration. Lectures will be in-person only; if there is an important reason you cannot make class, you may email Aidan Curtis (curtisa@mit.edu) to get a recording. phillipi at mit dot edu OH: Thu 2:30pm-3:30pm (2-146). stefje at csail dot mit dot edu OH: Thu 2:30pm-3:30

6.S898 Deep Learning, Fall 2023 --> MIT EECS 6.S898 Deep Learning Fall 2023 [ Schedule | Policies | Piazza | Canvas | Gradescope | Previous years ] [Final project blogs] --> Course Overview Description : Fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high-dimensions, and applications to computer vision, natural language processing, and robotics. Pre-requisites

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