Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

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  • Practical Aspects of Deep Learning
    • 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.
  • Optimization Algorithms
    • Develop your deep learning toolbox by adding more advanced optimizations, random minibatching, and learning rate decay scheduling to speed up your models.
  • Hyperparameter Tuning, Batch Normalization and Programming Frameworks
    • 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.