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 01/08 Topic: 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.
Lecture 2 01/15 Topic: Full-cycle of a deep learning project Completed modules:
  • C1M1: Introduction to deep learning (slides)
  • C1M2: Neural Network Basics (slides)
Quizzes (due 01/15 at 10am):
  • Introduction to deep learning
  • Neural Networks Basics
Programming Assignments (due 01/15 at 10am)
  • 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 01/22 Topic: Deep Learning Intuition (slides)
  • How to frame a machine learning problem?
  • How to choose your loss function?
  • Intuition behind various real-world application of deep learning.
Completed modules: Quizzes (due at 10am):
  • Shallow Neural Networks
  • Key concepts on Deep Neural Networks
Programming Assignments (due at 10am):
  • Planar data classification with a hidden layer
  • Building your Deep Neural Network: step by step
  • Deep Neural Network - Application
Project Proposal Due 01/22
Lecture 4 01/29 Topics:
  • Attacking neural networks with Adversarial examples (slides)
  • Generative Adversarial Networks (slides)
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)
Optional Video
  • Batch Normalization videos from C2M3 will be useful for the in-class lecture.
Quizzes (due at 10am):
  • Practical aspects of deep learning
  • Optimization Algorithms
Programming Assignments (due at 10am):
  • Initialization
  • Regularization
  • Gradient Checking
  • Optimization
Structuring Machine Learning Projects (Course 3)
Lecture 5 02/05 Topics:
  • AI in Health Care (Guest speaker: Pranav Rajpurkar)
  • Live-cell segmentation Case Study by Kian Katanforoosh (slides)
Completed modules:
  • C2M3: Hyperparameter Tuning, Batch Normalization (slides)
  • C3M1: ML Strategy (1) (slides)
  • C3M2: ML Strategy (2) (slides)
Quizzes (due at 10am):
  • Hyperparameter tuning, Batch Normalization, Programming Frameworks
  • Bird recognition in the city of Peacetopia (case study)
  • Autonomous driving (case study)
Programming Assignments (due at 10am):
  • Tensorflow
Midterm Review 02/08 Past midterms: Midterm Review Slides, Optimization and Initialization Slides, Adversarial Examples Notes, Adversarial Examples Jupyter Notebook
Convolutional Neural Networks (Course 4)
Lecture 6 02/12 Topic: Deep Learning Project strategy - Case studies Completed modules:
  • C4M1: Foundations of Convolutional Neural Network (slides)
  • C4M2: Deep Convolutional Models (slides)
Quizzes (due at 10am):
  • The basics of ConvNets
  • Convolutional models
Programming Assignments (due at 10am):
  • Convolutional Neural Network - Step by Step
  • Convolutional Neural Network - Application
  • Keras Tutorial: This assignment is optional.
  • Residual Networks
Midterm 02/13 Midterm
  • Date: Feburary 13, 2019
  • Time: 6 - 9pm
  • Locations: Cubberley Aud. and Bishop Aud.
Alternate Midterm
  • Date: February 14, 2019
  • Time: 6 - 9pm
  • Location: 370-370
Lecture 7 02/19 Topic: 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:
  • C4M3: ConvNets Applications (1) (slides)
  • C4M4: ConvNets Applications (2) (slides)
Quizzes (due at 10am):
  • Detection Algorithms
  • Special Applications: Face Recognition and Neural Style Transfer
Programming Assignments (due at 10am):
  • Car Detection with YOLOv2
  • Art Generation with Neural Style Transfer
  • Face recognition for the Happy House
Project Milestone Due 02/19 at 11:59pm Instructions
Sequence Models (Course 5)
Lecture 8 02/26 Topic:
  • Career Advice
  • Reading Research Papers
Optional Reading
Completed modules:
  • C5M1: Recurrent Neural Networks (slides)
Quizzes (due at 10am):
  • Recurrent Neural Networks
Programming Assignments (due at 10am):
  • Building a Recurrent Neural Network - Step by Step
  • Dinosaur Land -- Character-level Language Modeling
  • Jazz improvisation with LSTM
Lecture 9 03/05 Topic: 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 10am):
  • Natural Language Processing and Word Embeddings
  • Sequence Models and Attention Mechanism
Programming Assignments (due at 10am):
  • Operations on Word Vectors - Debiasing
  • Emojify!
  • Neural Machine Translation with Attention
  • Trigger Word Detection
Lecture 10 03/12 Topics:
  • Conversational Assistants (slides)
  • What's next?
Final Poster and Project Report Due 03/19
Tuesday, 11:59pm
Instructions for Poster and Project Report
Poster Session 03/20
Poster Session
  • Date: March 20, Wednesday
  • Time: 12:15pm - 2:15pm
  • Location: TBD