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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

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Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization
Deeplearning.ai Online Course Highlights
  • weeks long
  • 3-6 hours per week
  • Learn for FREE, Ugpradable
  • Self-Paced
  • Taught by: Andrew Ng, Head Teaching Assistant – Kian Katanforoosh, Teaching Assistant – Younes Bensouda Mourri
  • View Course Syllabus

Online Course Details:

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.

After 3 weeks, you will:
– 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.

This is the second course of the Deep Learning Specialization.

SKILLS YOU WILL GAIN

  • Hyperparameter
  • Tensorflow
  • Hyperparameter Optimization
  • Deep Learning

Take This Online Course