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Introduction to Deep Learning

Learn how deep learning algorithms can be used to solve important engineering problems.

There is one session available:

After a course session ends, it will be archived.
Started Aug 23
Ends Dec 19
Estimated 16 weeks
6–9 hours per week
Instructor-paced
Instructor-led on a course schedule
Free
Optional upgrade available

About this course

Skip About this course

This 3-credit-hour, 16-week course covers the fundamentals of deep learning. Students will gain a principled understanding of the motivation, justification, and design considerations of the deep neural network approach to machine learning and will complete hands-on projects using TensorFlow and Keras.

At a glance

  • Institution: PurdueX
  • Subject: Engineering
  • Level: Advanced
  • Prerequisites:

    This course is designed for students who have an undergraduate degree in electrical and computer engineering, computer science, or similar. Undergrauadate coursework in probabilistic methods in electrical and computer engineering and linear algebra is recommended before taking this course.

  • Language: English
  • Video Transcript: English

What you'll learn

Skip What you'll learn
  • Justify the development state-of-the-art deep learning algorithms.
  • Make design choices regarding the construction of deep learning algorithms.
  • Implement, optimize and tune state-of-the-art deep neural network architectures.
  • Identify and address the security aspects of state-of-the-art deep learning algorithms.
  • Examine open research problems in deep learning and propose approaches in the literature to tackle them.

Module 1: Introduction to Deep Feedforward Networks

    • Gradient-based learning
    • Sigmoidal output units
    • Back propagation

Module 2: Regularization for Deep Learning

    • Regularization strategies
    • Noise injection
    • Ensemble methods
    • Dropout

Module 3: Optimization for Training Deep Models

    • Optimization algorithms: Gradient, Hessian-Free, Newton
    • Momentum
    • Batch normalization

Module 4: Convolutional Neural Networks

    • Convolutional kernels
    • Downsampled convolution
    • Zero padding
    • Backpropagating convolution

Module 5: Recurrent Neural Networks

    • Recurrence relationship & recurrent networks
    • Long short-term memory (LSTM)
    • Back propagation through time (BPTT)
    • Gated and simple recurrent units
    • Neural Turing machine (NTM)

About the instructors

Who can take this course?

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