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Introduction to Deep Learning

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

  • 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.
  • Gradient-based learning
  • Sigmoidal output units
  • Back propagation
  • Regularization strategies
  • Noise injection
  • Ensemble methods
  • Dropout
  • Optimization algorithms: Gradient, Hessian-Free, Newton
  • Momentum
  • Batch normalization
  • Convolutional kernels
  • Downsampled convolution
  • Zero padding
  • Backpropagating convolution
  • Recurrence relationship & recurrent networks
  • Long short-term memory (LSTM)
  • Back propagation through time (BPTT)
  • Gated and simple recurrent units
  • Neural Turing machine (NTM)

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Length 16 weeks
Effort 6 - 9 hours per week
Starts On Demand (Start anytime)
Cost $2250
From Purdue University via edX
Instructor Aly El Gamal
Download Videos On all desktop and mobile devices
Language English
Tags Engineering

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Rating Not enough ratings
Length 16 weeks
Effort 6 - 9 hours per week
Starts On Demand (Start anytime)
Cost $2250
From Purdue University via edX
Instructor Aly El Gamal
Download Videos On all desktop and mobile devices
Language English
Tags Engineering

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