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.

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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 9/28 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 10/5 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 10/12 Wednesday 11:59 PM Instructions Meet with any TA between 9/26 and 10/12 to discuss your proposal.
Project Proposal Due 10/12 Wednesday 11:59 PM Instructions
Lecture 3 10/12 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 10/19 Topics: Full-cycle of a Deep Learning Project (no 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 10/26 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 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 11/2 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 11/2
Details posted on Ed soon
Lecture 7 11/9 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 11/11 Friday 11:59 PM Instructions Meet with your assigned TA between 10/7 and 11/11 to discuss your milestone report.
Project Milestone Due 11/11 Friday 11:59 PM Instructions
Sequence Models (Course 5)
Lecture 8 11/16 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 11/30 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 12/7 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 12/09 Friday 11:59 PM Instructions Meet with your assigned TA between 11/6 and 12/9 (before class) to discuss your final project report.
Project Final Report & Video Due 12/09 Friday 11:59 PM Instructions Please read over the final project guidelines here for information on the rubric and late submissions.