Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Intermediate Level
9 hours to complete
Flexible Schedule

Andrew Ng , Kian Katanforoosh , Younes Bensouda Mourri

Skills You’ll Gain

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

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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.