Skip to content

Latest commit

 

History

History
executable file
·
433 lines (427 loc) · 20.2 KB

syllabus.md

File metadata and controls

executable file
·
433 lines (427 loc) · 20.2 KB
Raw
layout comments keywords title description buttons micro_nav
default
false
Syllabus
For all "Materials and Assignments", follow the deadlines listed on this page, not on Coursera! Assignments are usually due every Tuesday, 30min before the class starts.
false

Announcements

  • The final project is now optional. See this piazza announcement for details.
  • Poster session is cancelled! See details at the bottom of the syllabus for how to submit the poster and presentation video. In short, the final report will be due Sunday midnight (3/15), and the poster and presentation video are due by Wednesday (3/18) noon. For more detail, see the main piazza announcement.

Syllabus

Event Date In-class lecture Online modules to complete Materials and Assignments
Lecture 1 01/07 Topics: (slides)
  • Class introduction
  • Examples of deep learning projects
  • Course details
 No online modules. If you are enrolled in CS230, you will receive an email on 01/07 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 01/14 Topics: Deep Learning Intuition (slides) Completed modules:
  • C1M1: Introduction to deep learning (slides)
  • C1M2: Neural Network Basics (slides)
Optional Video
  • Batch Normalization videos from C2M3 will be useful for the in-class lecture.
 Quizzes (due at 8:30am):
  • Introduction to deep learning
  • Neural Networks Basics
Programming Assignments (due at 8:30am)
  • Python Basics with Numpy (Optional)
  • Logistic Regression with a neural network mindset
Lecture 3 01/21 Topics: Full-cycle of a Deep Learning Project (no slides) Completed modules: Project TA meeting #1 deadline: meet with any TA to discuss proposal by Sun, 01/26
Quizzes (due at 8:30am):
  • Shallow Neural Networks
  • Key concepts on Deep Neural Networks
Programming Assignments (due at 8:30am):
  • Planar data classification with a hidden layer
  • Building your Deep Neural Network: step by step
  • Deep Neural Network - Application
Project Proposal Due {{ site.course.project_timeline.proposal | date: site.course.project_timeline.syllabus_date_format }} Instructions
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Course 2)
Lecture 4 01/28 Topics: Adversarial examples - GANs (slides)
  • Attacking neural networks with Adversarial Examples and Generative Adversarial Networks
Optional Readings: Explaining and Harnessing Adversarial Examples, Generative Adversarial Nets, Conditional GAN, Super-Resolution GAN, CycleGAN 		Completed modules:
  • C2M1: Practical aspects of deep learning (slides)
  • C2M2: Optimization algorithms (slides)
 Quizzes (due at 8:30am):
  • Practical aspects of deep learning
  • Optimization Algorithms
Programming Assignments (due at 8:30am):
  • Initialization
  • Regularization
  • Gradient Checking
  • Optimization
Structuring Machine Learning Projects (Course 3)
Lecture 5 02/04 Topics: AI and Healthcare. Guest Speaker: Pranav Rajpurkar. (guest slides) (main slides) Completed modules:
  • C2M3: Hyperparameter Tuning, Batch Normalization (slides)
  • C3M1: ML Strategy (1) (slides)
  • C3M2: ML Strategy (2) (slides)
 Quizzes (due at 8:30am):
  • Hyperparameter tuning, Batch Normalization, Programming Frameworks
  • Bird recognition in the city of Peacetopia (case study)
  • Autonomous driving (case study)
Programming Assignments (due at 8:30am):
  • Tensorflow
Convolutional Neural Networks (Course 4)
Lecture 6 02/11 Topics: Interpretability of Neural Networks (slides) Completed modules:
  • C4M1: Foundations of Convolutional Neural Network (slides)
  • C4M2: Deep Convolutional Models (slides)
 Quizzes (due at 8:30am):
  • The basics of ConvNets
  • Deep convolutional models
Programming Assignments (due at 8:30am):
  • Convolutional Model: step by step
  • Convolutional Model: application
  • Keras Tutorial: This assignment is optional.
  • Residual Networks
Midterm Review 02/13 Midterm review details:
  • Date & Time: Feb 13, 3:00-4:20pm
  • Location: Thornton 102
 Past midterms:
Lecture 7 02/18 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:
  • C4M3: ConvNets Applications (1) (slides)
  • C4M4: ConvNets Applications (2) (slides)
 Quizzes (due at 8:30am):
  • Detection Algorithms
  • Special Applications: Face Recognition & Neural Style Transfer
Programming Assignments (due at 8:30am):
  • Car Detection with YOLO
  • Art Generation with Neural Style Transfer
  • Face Recognition
Midterm {{ site.course.midterm_time }} Midterm details
Project Milestone Due {{ site.course.project_timeline.milestone | date: site.course.project_timeline.syllabus_date_format }} Instructions Project TA meeting #2 deadline: meet with your assigned project TA any time up until the milestone deadline.
Sequence Models (Course 5)
Lecture 8 02/25 Topics:
  • Career Advice
  • Reading Research Papers
Optional Reading 		Completed modules:
  • C5M1: Recurrent Neural Networks (slides)
 Quizzes (due at 8:30am):
  • Recurrent Neural Networks
Programming Assignments (due at 8:30am):
  • Building a Recurrent Neural Network - Step by Step
  • Dinosaur Land -- Character-level Language Modeling
  • Jazz improvisation with LSTM
Lecture 9 03/03 Topics: (slides)
  • Deep Reinforcement Learning

Optional Reading: 		Completed modules:
  • C5M2: Natural Language Processing and Word Embeddings (slides)
  • C5M3: Sequence-to-Sequence Models (slides)
 Quizzes (due at 8:30am):
  • Natural Language Processing and Word Embeddings
  • Sequence Models and Attention Mechanism
Programming Assignments (due at 8:30am):
  • Operations on Word Vectors - Debiasing
  • Emojify!
  • Neural Machine Translation with Attention
  • Trigger Word Detection
Lecture 10 03/10 Topics: (slides)
  • Class wrap-up
  • What's next?
 Optional:
  • If you’re interested in testing your ML/DL skills or preparing for job interviews in AI, you can take the Workera assessment
Final Project Report Due {{ site.course.project_timeline.poster_and_report | date: site.course.project_timeline.syllabus_date_format }} Instructions for Project Report Note: Late days cannot be applied to the final poster and report. Project TA meeting #3 deadline: meet with your assigned project TA any time up until the final report deadline (we suggest as soon as possible).
Final Poster and Video Due {{ site.course.project_timeline.poster_session }} Instructions for Poster NOTE: The poster session was cancelled. See Piazza Post for more details.