Deep Learning

CS230

Stanford School of Engineering

  • Fee:
    Fee may apply

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Description

"Artificial intelligence is the new electricity."

- Andrew Ng, Stanford Adjunct Professor 

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. 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 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.

Instructor(s)

Prerequisites

Students are expected to have the following background:
  • Familiarity with programming in Python and Linear Algebra (matrix / vector multiplications)
  • CS 229 may be taken concurrently

Topics include

  • Foundations of neural networks and deep learning
  • Techniques to improve neural networks: regularization and optimizations, hyperparameter tuning and deep learning frameworks (Tensorflow and Keras.)
  • Strategies to organize and successfully build a machine learning project
  • Convolutional Neural Networks, its applications (object classification, object detection, face verification, style transfer …) and related methods
  • Recurrent Neural Networks, its applications (natural language processing, speech recognition, …) and related methods
  • Advanced topics: Generative Adversarial Networks, Deep Reinforcement Learning, Adversarial Attacks
  • Insights from the AI industry, from academia, and advice to pursue a career in AI

Note on Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.

Notes

   
005 Spring 2018-19 Online

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Dates:April 2 - June 4, 2019
Days: Tue
Times:9:00 am - 10:20 am
Units: 3.00-4.00
Instructors:
Delivery Option:
Online
Fees:
For Credit $5,040.00 ?

Notes

Enrollment Dates: February 10 to March 18, 2019

Computer Science Department Requirement
Students taking graduate courses in Computer Science must enroll for the maximum number of units and maintain a B or better in each course in order to continue taking courses under the Non Degree Option.

This course may not currently be available to residents of certain states.