Announcements
- Please check out the FAQ for a list of changes to the course for the remote offering.
- Please join Ed during the first week. This is where the majority of course announcements will be found.
Syllabus
Modules are equivalent to “Weeks” in the Coursera courses. For example, C1M1 refers to C1 Week 1.
Event | Date | In-class lecture | Online modules to complete | Materials and Assignments |
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Lecture 1 | 4/5 |
Topics: (slides)
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No online modules. If you are enrolled in CS230, you will receive an email to join Course 1 ("Neural Networks and Deep Learning") on Coursera with your Stanford email. | No assignments. |
Neural Networks and Deep Learning (Course 1) | ||||
Lecture 2 | 4/12 | Topics: Deep Learning Intuition (slides) | Completed modules:
Optional Video
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Quizzes (due at 9 30 am PST (right before lecture)):
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Project Meeting #1 | 04/19 Wednesday 11:59 PM | Instructions | Meet with any TA between 4/3 and 4/19 to discuss your proposal. | |
Project Proposal Due | 04/19 Wednesday 11:59 PM | Instructions | ||
Lecture 3 | 4/19 |
Topics: Adversarial examples - GANs (slides)
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Completed modules: |
Quizzes (due at 9 30 am PST (right before lecture)):
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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Course 2) | ||||
Lecture 4 | 4/26 | Topics: AI and Healthcare. Guest Speaker: Pranav Rajpurkar. (guest slides) (main slides) | Completed modules: |
Quizzes (due at 9 30 am PST (right before lecture)):
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Structuring Machine Learning Projects (Course 3) | ||||
Lecture 5 | 5/3 | Topics: Full-cycle of a Deep Learning Project (no slides) | Completed modules: |
Quizzes (due at 9 30 am PST (right before lecture)):
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Convolutional Neural Networks (Course 4) | ||||
Lecture 6 | 5/10 |
Topics: Deep Learning Strategy (no slides)
Optional Reading: A guide to convolution arithmetic for deep learning, Is the deconvolution layer the same as a convolutional layer?, Visualizing and Understanding Convolutional Networks, Deep Inside Convolutional Networks: Visualizing Image Classification Models and Saliency Maps, Understanding Neural Networks Through Deep Visualization, Learning Deep Features for Discriminative Localization |
Completed modules: |
Quizzes (due at 9 30 am PST (right before lecture)):
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Midterm Review | TBD | Past midterms:
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Midterm | 5/10 |
Details posted on Ed soon |
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Lecture 7 | 5/17 | Topics: Interpretability of Neural Networks (slides) | Completed modules: |
Quizzes (due at 9 30 am PST (right before lecture)):
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Project Meeting #2 | 05/19 Friday 11:59 PM | Instructions | Meet with your assigned TA between 4/19 and 5/19 to discuss your milestone report. | |
Project Milestone Due | 05/19 Friday 11:59 PM | Instructions | ||
Sequence Models (Course 5) | ||||
Lecture 8 | 5/24 |
Topics:
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Completed modules:
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Quizzes (due at 9 30 am PST (right before lecture)):
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Lecture 9 | 5/31 |
Topics:
(slides)
Optional Reading: |
Completed modules: |
Quizzes (due at 9 30 am PST (right before lecture)):
|
Lecture 10 | 6/7 |
Topics: (slides)
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Optional:
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Project Meeting #3 | 06/07 Wednesday 11:59 PM | Instructions | Meet with your assigned TA between 5/19 and 6/7 (before class) to discuss your final project report. | |
Project Final Report & Video Due | 06/07 Wednesday 11:59 PM | Instructions | Please read over the final project guidelines here for information on the rubric and late submissions. |