Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

📚 🎯 machine learning

    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.

    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.

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