Syllabus

For all "Materials and Assignments", follow the deadlines listed on this page, not on Coursera! Assignments are usually due every Wednesday 9:30 am PST, right before the weekly class.

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
Lecture 1 4/5 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 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:
  • 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 9 30 am PST (right before lecture)):
  • Introduction to deep learning
  • Neural Networks Basics
Programming Assignments (due at 9 30 am PST (right before lecture))
  • Python Basics with Numpy (Optional)
  • Logistic Regression with a neural network mindset
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)
  • 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: Quizzes (due at 9 30 am PST (right before lecture)):
  • Shallow Neural Networks
  • Key concepts on Deep Neural Networks
Programming Assignments (due at 9 30 am PST (right before lecture)):
  • Planar data classification with a hidden layer
  • Building your Deep Neural Network: step by step
  • Deep Neural Network - Application
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:
  • C2M1: Practical aspects of deep learning (slides)
  • C2M2: Optimization algorithms (slides)
 Quizzes (due at 9 30 am PST (right before lecture)):
  • Practical aspects of deep learning
  • Optimization Algorithms
Programming Assignments (due at 9 30 am PST (right before lecture)):
  • Initialization
  • Regularization
  • Gradient Checking
  • Optimization
Structuring Machine Learning Projects (Course 3)
Lecture 5 5/3 Topics: Full-cycle of a Deep Learning Project (no slides) Completed modules:
  • C2M3: Hyperparameter Tuning, Batch Normalization (slides)
  • C3M1: ML Strategy (1) (slides)
  • C3M2: ML Strategy (2) (slides)
 Quizzes (due at 9 30 am PST (right before lecture)):
  • Hyperparameter tuning, Batch Normalization, Programming Frameworks
  • Bird recognition in the city of Peacetopia (case study)
  • Autonomous driving (case study)
Programming Assignments (due at 9 30 am PST (right before lecture)):
  • Tensorflow
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:
  • C4M1: Foundations of Convolutional Neural Network (slides)
  • C4M2: Deep Convolutional Models (slides)
 Quizzes (due at 9 30 am PST (right before lecture)):
  • The basics of ConvNets
  • Deep convolutional models
Programming Assignments (due at 9 30 am PST (right before lecture)):
  • Convolutional Model: step by step
  • Convolutional Model: application
  • Residual Networks
  • Transfer Learning with MobileNet
Midterm Review TBD Past midterms:
Midterm 5/10
Details posted on Ed soon
Lecture 7 5/17 Topics: Interpretability of Neural Networks (slides) Completed modules:
  • C4M3: ConvNets Applications (1) (slides)
  • C4M4: ConvNets Applications (2) (slides)
 Quizzes (due at 9 30 am PST (right before lecture)):
  • Detection Algorithms
  • Special Applications: Face Recognition & Neural Style Transfer
Programming Assignments (due at 9 30 am PST (right before lecture)):
  • Car Detection with YOLO
  • Image Segmentation with U-Net
  • Art Generation with Neural Style Transfer
  • Face Recognition
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:
  • Career Advice
  • Reading Research Papers
Optional Reading 		Completed modules:
  • C5M1: Recurrent Neural Networks (slides)
 Quizzes (due at 9 30 am PST (right before lecture)):
  • Recurrent Neural Networks
Programming Assignments (due at 9 30 am PST (right before lecture)):
  • Building a Recurrent Neural Network - Step by Step
  • Dinosaur Land -- Character-level Language Modeling
  • Jazz improvisation with LSTM
Lecture 9 5/31 Topics: (slides)
  • Deep Reinforcement Learning

Optional Reading: 		Completed modules:
  • C5M2: Natural Language Processing and Word Embeddings (slides)
  • C5M3: Sequence-to-Sequence Models (slides)
  • C5M4: Transformer Network (Optional)
 Quizzes (due at 9 30 am PST (right before lecture)):
  • Natural Language Processing and Word Embeddings
  • Sequence Models and Attention Mechanism
  • Transformers (Optional)
Programming Assignments (due at 9 30 am PST (right before lecture)):
  • Operations on Word Vectors - Debiasing
  • Emojify!
  • Neural Machine Translation with Attention
  • Trigger Word Detection
  • Transformers Architecture with TensorFlow (Optional)
  • Transformer Pre-processing (Optional)
  • Transformer Network Application: Named-Entity Recognition (Optional)
  • Transformer Network Application: Question Answering (Optional)
Lecture 10 6/7 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
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.