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.

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Syllabus

Event Date In-class lecture Online modules to complete Materials and Assignments
Lecture 1 04/07 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 04/07 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 04/14 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 9am PST):
  • Introduction to deep learning
  • Neural Networks Basics
Programming Assignments (due at 9am PST)
  • Python Basics with Numpy (Optional)
  • Logistic Regression with a neural network mindset
Lecture 3 04/21 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 9am PST):
  • Shallow Neural Networks
  • Key concepts on Deep Neural Networks
Programming Assignments (due at 9am PST):
  • Planar data classification with a hidden layer
  • Building your Deep Neural Network: step by step
  • Deep Neural Network - Application
Project Meeting #1 04/22 Wednesday 11:59 PM Instructions Meet with any TA between 4/06 and 4/22 to discuss your proposal.
Project Proposal Due 04/22 Wednesday 11:59 PM Instructions
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Course 2)
Lecture 4 04/28 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 9am PST):
  • Practical aspects of deep learning
  • Optimization Algorithms
Programming Assignments (due at 9am PST):
  • Initialization
  • Regularization
  • Gradient Checking
  • Optimization
Structuring Machine Learning Projects (Course 3)
Lecture 5 05/05 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 9am PST, WED 5/6 (due to Coursera downtime)):
  • Hyperparameter tuning, Batch Normalization, Programming Frameworks
  • Bird recognition in the city of Peacetopia (case study)
  • Autonomous driving (case study)
Programming Assignments (due at 9am PST, WED 5/6 (due to Coursera downtime)):
  • Tensorflow
Project Meeting #2 05/08 Friday 11:59 PM Instructions Meet with your assigned TA between 4/23 and 5/08 to discuss your first milestone report.
Project Milestone #1 Due 05/08 Friday 11:59 PM Instructions
Convolutional Neural Networks (Course 4)
Lecture 6 05/12 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 9am PST):
  • The basics of ConvNets
  • Deep convolutional models
Programming Assignments (due at 9am PST):
  • Convolutional Model: step by step
  • Convolutional Model: application
  • Keras Tutorial: This assignment is optional.
  • Residual Networks
Lecture 7 05/19 Topics: Interpretability of Neural Networks (slides) Completed modules:
  • C4M3: ConvNets Applications (1) (slides)
  • C4M4: ConvNets Applications (2) (slides)
Quizzes (due at 9am PST):
  • Detection Algorithms
  • Special Applications: Face Recognition & Neural Style Transfer
Programming Assignments (due at 9am PST):
  • Car Detection with YOLO
  • Art Generation with Neural Style Transfer
  • Face Recognition
Project Meeting #3 05/24 Sunday 11:59 PM Instructions Meet with your assigned TA between 5/09 and 5/24 to discuss your second milestone report.
Project Milestone #2 Due 05/24 Sunday 11:59 PM Instructions
Sequence Models (Course 5)
Lecture 8 05/26 Topics:
  • Career Advice
  • Reading Research Papers
Optional Reading
Completed modules:
  • C5M1: Recurrent Neural Networks (slides)
Quizzes (due at 9am PST):
  • Recurrent Neural Networks
Programming Assignments (due at 9am PST):
  • Building a Recurrent Neural Network - Step by Step
  • Dinosaur Land -- Character-level Language Modeling
  • Jazz improvisation with LSTM
Lecture 9 06/02 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 9am PST):
  • Natural Language Processing and Word Embeddings
  • Sequence Models and Attention Mechanism
Programming Assignments (due at 9am PST):
  • Operations on Word Vectors - Debiasing
  • Emojify!
  • Neural Machine Translation with Attention
  • Trigger Word Detection
Lecture 10 06/09 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 #4 06/09 Tuesday 10:20 AM Instructions Meet with your assigned TA between 5/23 and 6/9 (before class) to discuss your final project report.
Project Final Report & Video Due 06/09 Tuesday 10:20 AM Instructions Please read over the final project guidelines here for information on the rubric and late submissions.