Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Rated 4.9 out of 5 based on 89 customer ratings
Coursera

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

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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