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.

Event Date In-class lecture Online modules to complete Materials and Assignments
Neural Networks and Deep Learning (Course 1)
Lecture 1 09/24 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 09/25 to join Course 1 ("Neural Networks and Deep Learning") on Coursera with your Stanford email. No assignments.
Lecture 2 10/01 Topics: Practical Approaches to Deep Learning Projects (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
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Course 2)
Lecture 3 10/8 Topics: Adversarial examples - GAN
  • Attacking neural networks with Adversarial Examples and Generative Adversarial Networks (slides)
Optional Readings: Explaining and Harnessing Adversarial Examples, Generative Adversarial Nets, Conditional GAN, Super-Resolution GAN, CycleGAN 		Completed modules: 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 10/10 Thursday 11:59 PM Instructions
Lecture 4 10/15 Topics: Full-cycle DL Projects Completed modules:
  • C2M1: Practical aspects of deep learning (slides)
  • C2M2: Optimization algorithms (slides)
 Note: Meeting with your project TA is mandatory this week! 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 10/22 Topics: DL Strategy 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
Lecture 6 10/29 Topics: Interpretability of Neural Network (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 8:30am):
  • The basics of ConvNets
  • Convolutional models
Programming Assignments (due at 8:30am):
  • Convolutional Neural Network - Step by Step
  • Convolutional Neural Network - Application
  • Keras Tutorial: This assignment is optional.
  • Residual Networks
Midterm Review 10/31 3:00-4:20pm in Thornton 102 Past midterms:
Convolutional Neural Networks (Course 4)
Lecture 7 11/05 Topics: AI and Healthcare (Guest Speaker: Pranav Rajpurkar) (slides) Completed modules:
  • C4M3: ConvNets Applications (1) (slides)
  • C4M4: ConvNets Applications (2) (slides)
 Quizzes (due on Friday 11/08 at 11:59pm):
  • Detection Algorithms
  • Special Applications: Face Recognition and Neural Style Transfer
Programming Assignments (due on Friday 11/08 at 11:59pm):
  • Car Detection with YOLOv2
  • Art Generation with Neural Style Transfer
  • Face recognition for the Happy House
Project Milestone Due 11/05 Tuesday 11:59 PM Instructions
Midterm 11/06 Wednesday 6-9 PM Midterm Alternate Midterm
(Only for students with valid, approved reason)
  • Date: TBD
Sequence Models (Course 5)
Lecture 8 11/12 Topics:
  • Career Advice
  • Reading Research Papers
Optional Reading 		Completed modules:
  • C5M1: Recurrent Neural Networks (slides)
 Note: Meeting with your project TA is mandatory; you have 2 weeks to meet
Quizzes (due on Thursday 11/14 at 8:30am):
  • Recurrent Neural Networks
Programming Assignments (due on Thursday 11/14 at 8:30am):
  • Building a Recurrent Neural Network - Step by Step
  • Dinosaur Land -- Character-level Language Modeling
  • Jazz improvisation with LSTM
Lecture 9 11/19 Topics:
  • Deep Reinforcement Learning
  • Slides

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 12/03 Topics:
  • Class wrap-up
  • What's next?
  • Slides
 Note: Meeting with your project TA is optional this week
Final Poster and Project Report Due 12/08 Sunday 11:59 PM Instructions for Poster and Project Report Note: Late days cannot be applied to the final poster and report.
Poster Session 12/11 Wednesday 8:30 AM - 11:30 AM Poster Session Location: ACSR Basketball Courts (341 Galvez St.)