Stanford Root

Schedule

Stanford Root

Schedule

CS 230

Deep Learning

UNITS:3-4
GRADING:Letter or Credit/No Credit
LEVEL:Graduate
GER:WAY-AQR, WAY-FR

Deep Learning is one of the most highly sought after skills in AI. We will help you become good at Deep Learning. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. AI is transforming multiple industries. After this course, you will likely find creative ways to apply it to your work. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come in to class for advanced discussions and work on projects. This class will culminate in an open-ended final project, which the teaching team will help you on. Prerequisites: Familiarity with programming in Python and Linear Algebra (matrix / vector multiplications). CS 229 may be taken concurrently.

Syllabus for selected term:
View Autumn 2026 Syllabus

Sections

3 Terms
Lecture 1Open
ID: 2154
0 / 300 enrolled
DAYS:Tuesday
TIME:9 AM – 10:50 AM
LOCATION:NVIDIA Auditorium
INSTRUCTOR:
Ng, Andrew, Katanforoosh, Kian
units

CS 230: Deep Learning

3-4 units · Letter or Credit/No Credit · GER: WAY-AQR, WAY-FR

Deep Learning is one of the most highly sought after skills in AI. We will help you become good at Deep Learning. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. AI is transforming multiple industries. After this course, you will likely find creative ways to apply it to your work. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come in to class for advanced discussions and work on projects. This class will culminate in an open-ended final project, which the teaching team will help you on. Prerequisites: Familiarity with programming in Python and Linear Algebra (matrix / vector multiplications). CS 229 may be taken concurrently.

Offered in Autumn 2026, Winter 2027, Spring 2027 at Stanford University.

Autumn 2026 sections

  • Lecture — Tuesday 9:00 AM – 10:50 AM — NVIDIA Auditorium — Ng, Andrew, Katanforoosh, Kian (Graduate)

Winter 2027 sections

  • Discussion — TBA TBA (Graduate)
  • Lecture — TBA TBA (Graduate)

Spring 2027 sections

  • Lecture — TBA TBA (Graduate)
  • Discussion — TBA TBA (Graduate)

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  • CS 229: Machine Learning (STATS 229)
  • CS 231A: Computer Vision: From 3D Perception to 3D Reconstruction and Beyond
  • CS 231N: Deep Learning for Computer Vision
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