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Fundamentals of Deep Learning

What Will Learn?

  • Course Aims
  • Course Goals
  • Decent Work and Economic Growth
  • Industry, Innovation and Infrastructure
  • Sustainable Cities and Communities

Requirements

CSE374s

Description

  • English Description

    Introduction to deep learning and its underlying theory. Architectures commonly associated with deep learning: Basic Neural Networks (NN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN). Methods to train and optimize the architectures. Methods to perform effective inference. Range of applications.
  • Arabic Description

    Introduction to deep learning and its underlying theory. Architectures commonly associated with deep learning: Basic Neural Networks (NN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN). Methods to train and optimize the architectures. Methods to perform effective inference. Range of applications.
  • Department
    Computer and Systems Engineering
  • Credit Hours
    2
  • Grades
    Total ( 100 ) = Midterm (20) + tr.Student Activities (30 = tr.Industry 0% , tr.Project 10% , tr.Self_learning 0% , tr.Seminar 20% ) + Exam Grade (50)
  • Hours
    Lecture Hours: 2, Tutorial Hours: 1, Lab Hours: 0
  • Required SWL
    100
  • Equivalent ECTS
    4
  • - Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning. MIT Press, 2016. online version
  • - Zhang, Aston and Lipton, Zachary C. and Li, Mu and Smola, Alexander J, “Dive into Deep Learning”, arXiv:2106.11342, 2021.
  • - Sandro Skansi, Introduction to Deep Learning. Springer, 2018. - Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning. MIT Press, 2016. online version .