CS230: Deep Learning - Lecture Notes

Course Introduction

• Course Title: CS230, Deep Learning
• Instructor: Class involves Andrew Ng, Kian Katanforosh, and multiple TAs.
• Course Format:
  • Flipped classroom
  • Interactive class with in-depth discussions
  • Online content from deeplearning.AI on Coursera

Course Team

• Co-Instructors:
  • Kian Katanforosh: Co-creator of the Deep Learning specialization
  • Younes Mourri: Course adviser
• Class Coordinator: Swati Dubei
• Head TAs: Aarti Bagul and Abhijeet
• TA Expertise Areas: Healthcare, robotics, computational biology, etc.

Deep Learning Overview

Importance and Growth

• Deep Learning is a rapidly advancing area in AI and computer science.
• The rise due to increased data availability and computational power (e.g., GPUs).
• Performance improvement with large neural networks compared to traditional algorithms.

AI Tools Beyond Deep Learning

• Other AI Tools: Probabilistic graphical models, planning algorithms, search algorithms, knowledge representation, game theory.
• Deep Learning has seen the most rapid improvement due to data, computation, and investment.

Practical Applications

• Real-world applications include web search, online recommendations, fraud detection, email filtering, and more.

Course Objectives

1. Become Experts in Deep Learning Algorithms: Learn state-of-the-art techniques.
2. Application of Algorithms: Apply these techniques to real-world problems.
3. Practical Know-how: Gain practical knowledge for implementing and deploying models efficiently.

Course Structure and Logistics

• Weekly Schedule:

  • Watch online videos, complete quizzes, and programming assignments.
  • Attend in-class lectures and TA sections.
  • Participate in personalized mentorship.

• Grading:

  • Attendance: 2%
  • Quizzes: 8%
  • Programming Assignments: 25%
  • Midterm: 20%
  • Final Project: 45%

Programming Assignments and Projects

Assignments

• Sign Language Translation using logistic regression and CNNs.
• Happy House Algorithm for mood detection.
• Object Detection with YOLO v2.
• Goalkeeper Shoot Prediction, Car Detection for autonomous driving.
• Face Recognition, Art Generation, Music Generation.
• Machine Translation, and Trigger Word Detection.

Projects

• Encourage diverse and innovative projects that incorporate deep learning.
• Examples: Colorizing black-and-white photos, predicting product prices, healthcare diagnostics, etc.

Career and Industry Insights

AI as the New Electricity

• AI's potential to transform industries similarly to electricity.
• Opportunities in non-tech industries like healthcare, education, and more.

Organizing AI Teams

• Importance of organizing AI teams effectively to leverage modern AI tools.
• Strategic data acquisition, unified data warehouses, and new job descriptions in AI.

Additional Resources

• Machine Learning Yearning: Book by Andrew Ng with best practices for ML.
• Coursera Content: Supplement lectures with deep learning videos and exercises.

Conclusion and Next Steps

• Immediate Tasks:
  • Create Coursera account and start modules.
  • Form teams for project work by Friday.
• Mentorship: Regular weekly check-ins for project guidance.