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Practicum for Deep Neural Networks

Deep Neural Networks (DNN) have revolutionized the field of artificial intelligence over the past decade, driving significant advancements in "state of the art" AI technologies. Since this course was first introduced in 2018, the field has evolved substantially. In response, the summer 2024 syllabus includes refreshed content and new topics aligned with the latest DNN architectures.

This course provides an introduction to the fundamental concepts of DNN development, covering key architectures and algorithms through a variety of neural network models. Students will engage in detailed analyses of leading deep learning approaches, working with popular networks such as Convolutional Neural Networks (CNN), Fully Connected Neural Networks (FCNN), ImageNet, ResNet, and Recurrent Neural Networks (RNN). Additionally, the course will explore new Generative AI models, with a focus on the "Transformers" architecture foundational to systems like GPT, Copilot, Gemini, and Llama.

Practical exercises using the two most popular AI tools/frameworks will help students develop hands-on skills and confidence in applying machine learning concepts within neural network contexts. By the end of this course, students will have acquired essential deep learning knowledge and skills to create and train neural networks for practical applications.

Learning Outcomes:

  • Understand the architecture of deep neural networks (DNN), including different families of DNN models and the taxonomy of DNN architectures.
  • Build and train a multilayer perceptron, adapting it to include different layer architectures and comparing performance.
  • Apply convolutional neural networks (CNN) in image classification and segmentation tasks.
  • Utilize DNNs in natural language processing applications.
  • Introduce the concept of Generative AI, focusing on autoregressive model architecture.
  • Explore Transformers architecture with a practical walkthrough for building a GPT model for text generation.
The goal of this course is to open career opportunities in the highly sought-after fields of machine learning and AI. The key topics covered will provide insights into how AI can revolutionize your career. Students will also gain a better understanding of the advantages and limitations of Generative AI.

Course typically offered: Online during our Summer and Winter academic quarters.

Prerequisites: CSE-41287: Linear Algebra for Machine Learning and CSE-41305: Probability and Statistics for Deep Learning, or equivilent knowledge.

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

Course Number: CSE-41311
Credit: 3.00 unit(s)
Related Certificate Programs: Machine Learning Methods

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