Deep Networks have revolutionized computer vision, language technology, robotics and control. They have a growing impact in many other areas of science and engineering, and increasingly, on commerce and society. They do not however, follow any currently known compact set of theoretical principles. In Yann Lecun's words they require "an interplay between intuitive insights, theoretical modeling, practical implementations, empirical studies, and scientific analyses." This is a fancy way of saying “we don’t understand this stuff nearly well enough, but we have no choice but to muddle through anyway.” This course attempts to cover that ground and show you how to muddle through even as we aspire to do more.
Lectures are webcast by the department and recordings will be posted to this youtube playlist. You must be logged into your @berkeley.edu account to access the videos. Lectures will have a substantial amount (if not all) of the content covered on the whiteboard (not on presentation slides)
Because Deep Learning is rapidly evolving field, the material covered in this course can change substantially from semester to semester. If you are interested in materials from previous iterations of this course, please see here: [Sp21] [Sp22] [Fa22]
W | Date | Lecture Topic | Resources | Section | Homework |
---|---|---|---|---|---|
0 | Jan 20 | Introduction |
HW0 - Basics Written Coding Solution (Written) Self-Grade |
||
1 | Jan 23 | Basic Principles | Fa22 Notes Note 2 Scribe Notes | Worksheet Code Solution |
HW1 - Math Review & Fully-Connected Network Written Coding 1 Coding 2 Solution (Written) Self-Grade |
Jan 27 | Basic Principles, Optimizers | Fa22 Notes Note 3 Scribe Notes | |||
2 | Jan 30 | Initialization and Normalization | Fa22 Notes Scribe Notes | Worksheet Solution |
HW2 - Optimization and Initialization Written Coding (Q2) Coding (Q5) Coding (Q6) Solution (Written) Self-Grade |
Feb 3 | Survey of Problems and Architectures | Fa22 Notes Note 5 Scribe Notes | |||
3 | Feb 6 | Survey of Problems and Architectures | Fa22 Notes Note 6 Scribe Notes | Worksheet Code Solution |
HW3 - Batch Normalization, Dropout, and Convolutions Written Coding (Q4) Coding (Q5-1) Coding (Q5-2) Coding (Q5-3) Solution (Written) Self-Grade |
Feb 10 | ConvNets and Computer Vision | Fa22 Notes Scribe Notes | |||
4 | Feb 13 | ConvNets and Computer Vision | Fa22 Notes Scribe Notes | Worksheet Code Solution |
HW4 - Dropout and Convolutions (II) Written Coding (Q1) Coding (Q4) Coding (Q5) Solution (Written) Self-Grade |
Feb 17 | Computer Vision in Practice | Fa22 Notes Scribe Notes | |||
5 | Feb 20 | Holiday | Fa22 Notes Scribe Notes | Worksheet Solution |
HW5 - Graph Neural Networks Written Coding (Q3) Coding (Q5) Solution (Written) Self-Grade |
Feb 24 | Dropout, Graph Neural Networks | Fa22 Notes Scribe Notes | |||
6 | Feb 27 | Graph Neural Networks | Fa22 Notes | Worksheet Solution |
HW6 - Recurrent Neural Networks Written Coding (Q3) Coding (Q4) Solution (Written) Self-Grade |
Mar 3 | Recurrent Neural Networks | Fa22 Notes | |||
7 | Mar 6 | RNNs and LSTMs | Fa22 Notes | Worksheet Solution |
HW7 - Autoencoders and Attention Written Coding Solution (Written) Self-Grade |
Mar 10 | Seq-to-Seq, Attention | Fa22 Notes | |||
8 | Mar 13 | Self-Supervision, Autoencoders | Fa22 Notes | Worksheet Solution | Study for Midterm |
Mar 17 | Transformers | Fa22 Notes | |||
9 | Mar 20 | No Lecture: Study for Midterm | Fa22 Notes | Worksheet (Basics) Worksheet (CNN&GNN) Worksheet (AE&RNN) Solution (Basics) Solution (CNN&GNN) Solution (AE&RNN) | Midterm |
Mar 24 | Transformers | Fa22 Notes | |||
Mar 27 | Spring Break | ||||
Mar 31 | Spring Break | ||||
10 | Apr 3 | Meta-learning, fine-tuning, transfer | Fa22 Notes | ||
Apr 7 | Meta-learning, fine-tuning, transfer | Fa22 Notes | |||
11 | Apr 10 | Meta-learning, fine-tuning, transfer | Fa22 Notes | ||
Apr 14 | Generative Models | Fa22 Notes | |||
12 | Apr 17 | Generative Models | Fa22 Notes | ||
Apr 21 | Generative Models | Fa22 Notes | |||
13 | Apr 24 | Advanced Topics | Fa22 Notes | ||
Apr 28 | Conclusion and Review | Fa22 Notes | |||
14 | May 1 | RRR Week | |||
May 5 | RRR Week | ||||
15 | May 8 | Final Exam: Mon, May 8, 7pm - 10pm |