Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Instructor: Andrew Ng , Kian Katanforoosh , Younes Bensouda Mourri

Intermediate Level • 9 hours to complete • Flexible Schedule

Skills You'll Gain

Tensorflow
Debugging
Analysis
Artificial Intelligence and Machine Learning (AI/ML)
Machine Learning Algorithms
Deep Learning
Performance Tuning
Artificial Neural Networks

Shareable Certificate

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Outcomes

  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 3 modules in this course

Discover and experiment with a variety of different initialization methods, apply L2 regularization and dropout to avoid model overfitting, then apply gradient checking to identify errors in a fraud detection model.

Develop your deep learning toolbox by adding more advanced optimizations, random minibatching, and learning rate decay scheduling to speed up your models.

Explore TensorFlow, a deep learning framework that allows you to build neural networks quickly and easily, then train a neural network on a TensorFlow dataset.