CSE590/MB590 Special Topics
  - Artificial Intelligence Application Using TensorFlow

This course will teach the fundamentals and contemporary usage of the TensorFlow library for deep learning projects. The goal is to help students understand the graphical computational model of TensorFlow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project.
  • » 24 hours in class lecturing plus dedicated mentoring sessions from our faculty of industry experts


  • » 1.5 semester credits for both certificate and master’s degree


  • » Access to high-quality live class recording


  • » Online live classroom available for all classes


  • » Lifetime learning resources for our students


  • $ 1000
Course Description

This course will teach the fundamentals and contemporary usage of the TensorFlow library for deep learning projects. The goal is to help students understand the graphical computational model of TensorFlow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project.

Course Objectives
The main content of the course includes the following parts:
  • TensorFlow basics
  • Linear and Logistic Regression and TensorFlow Serving
  • DNN (Deep Neural Network), regularization, dropout, hyper-parameter tuning
  • CNN (Convolutional neural network)
  • RNN (Recurrent Neural Networks), LSTM and Seq2seq
  • Reinformance Learning
Outcomes

Through the teaching, students will use TensorFlow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments.

Your Instructor
 
 
 
 
 
 
 
 
 
 
 Danian Gong
Currently work at CADENCE Design Systems as Sr. Principle Engineer, responsible for developing high performance microprocessor; also serves as adjunct professor in CSTU, where he teaches Artificial intelligence and deep learning.
Study and work Experience:
1996 Graduate from Zhejiang University, Hanzhou,bachelor degree in EE major.
2001 Graduate from Tsinghua University, Beijing, Ph.D and master degree in EE major.
2001-present, R&D management and development in Silicon Valley High Tech companies