Deep Learning Using TensorFlow

Deep Learning is a branch of Artificial Intelligence (AI) that relies on the architecture of Neural Networks. When the number of hidden layers in a neural network is increased, it transforms into a ‘Deep Learning’ Neural Network. This course begins by explaining the concepts of Neural Networks and Deep Learning. Subsequently, it delves into the basics of the Python language and TensorFlow. The process of installing TensorFlow, Keras, and other interfaces is covered. Following that, it explores various Machine Learning modeling techniques for both estimation and classification tasks. Additionally, the architecture of GPU and TPU is discussed. By undertaking this course, students can significantly enhance their prospects in the rapidly growing 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 Spring and Fall quarters

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