Units
3.0 QUARTER UNITS

Course Description


Deep learning, a branch of artificial intelligence and machine learning, uses multilayered neural networks to create highly accurate prediction models for image recognition, object detection, language translation, speech recognition, and other tasks. In this course, students will use open source and industry-standard machine learning libraries to build and deploy deep learning models.

 

Students will build deep learning prediction models of different complexities, from simple linear logistic regression to major categories of neural networks including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTMs), and gated recurrent units (GRUs).

By the end of the course, students will be proficient in best practices of using standard machine learning frameworks such as Pytorch, TensorFlow and Keras, and using datasets for solving common machine learning problems.

The class prepares students to pursue a career in data sciences and AI model development.

 

Topics

  • Deep learning with standard machine learning frameworks including TensorFlow, Keras and Pytorch
  • Multilayer perceptrons
  • Advanced multilayer perceptrons
  • Convolutional neural networks
  • Image processing CNN architectures
  • Recurrent neural networks
  • RNN - prediction with multilayer perceptron
  • RNN - prediction with long short term memory networks

 


Note(s):

Students are required to bring laptops for the classroom and work with Python3/ Jupyter Notebook environment.

Prerequisites / Skills Needed

 


Moderate level of computer programming ability in Python, comfortable with an editor, familiarity with command-line operations on a laptop, and a basic understanding of Machine Learning models.

 

Additional Information

AI* - This class empowers students to harness the power of AI by learning how intelligent systems are designed, trained, and applied across real-world challenges. Students will use AI-driven tools and techniques to gain practical, industry-ready experience in building and deploying deep learning models. 

Syllabus Library
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