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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

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Course Highlights

  • 16 HOURS of E-learning content, tests etc

  • Skills Covered: Hyperparameter, Tensorflow, Hyperparameter Optimization, Deep Learning

  • Includes Peer Graded Assignments, Quizzes

  • Learn from top Instructor: Andrew Ng

  • 37% got a tangible career benefit from this course

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Mode of learning: Online self study

Course Overview

What are the course deliverables ?
  • Understand industry best-practices for building deep learning applications.

  • Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking,

  • Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.

  • Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance

  • Be able to implement a neural network in TensorFlow.

More about this course
  • This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow.

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Curriculum

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    Reviews

    Average Rating

    4.8

    Based on 1 Ratings

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    Saumya Sinha

    It was a great experience As I am studying it for my BE project.It helped me a lot.I would like to thank Mr. Andrew Ng for his teaching.I personally found it very helpful.
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