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Deep Learning Using TensorFlow

Deep Learning is a branch of Artificial Intelligence (AI) that is based on the architecture of Neural Networks. When the number of hidden layers in a neural network is extended, it becomes a ‘Deep Learning’ Neural Network. This course will first cover the concepts of Neural Networks and Deep Learning. Next, it will cover the basic of the Python language and TensorFlow. The procedure of installing TensorFlow, Keras, and other interfaces will be examined. Then, it will test all Machine Learning modeling methods (estimation and classification). The architecture of GPU and TPU will also be addressed. By taking this course, students will dramatically enhance their prospects for a career in the hot AI market.

Learning Outcomes:

  • Understand how Neural Networks become the foundational architecture of Deep Learning
  • Review tools available to build Deep Learning including: Tensor Flow, Keras, and Theano
  • How to install TensorFlow in the Python environment
  • Understand the GUI (Graphical User Interface) of interface software and how it interfaces with TensorFlow
  • Review Machine Learning models that can be implemented
  • Build Deep Learning Machine Learning models using TensorFlow and various interfaces

Course typically offered: Online in Winter and Spring.

Prerequisites: Introduction to Programming (CSE-40028) or a basic working knowledge of Python. Students must have access to a web-enabled computer.

Next Steps: Upon completion, consider additional coursework in our specialized certificate in Machine Learning Methods to continue learning.

More Information: For more information about this course, please contact unex-techdata@ucsd.edu.

Course Number: CSE-41312
Credit: 3.00 unit(s)
Related Certificate Programs: Machine Learning Methods

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