• Length:
    16 Weeks
  • Effort:
    6–9 hours per week
  • Price:

    Add a Verified Certificate for $2,250 USD

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  • Video Transcript:
  • Course Type:
    Instructor-led on a course schedule


Knowledge of probabilistic methods in electrical and computer engineering (ECE 302 at Purdue) and undergraduate linear algebra.

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.

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)

Meet your instructors

Aly El Gamal
Assistant Professor, Electrical and Computer Engineering
Purdue University

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