Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Coursera)

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Coursera)
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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (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.

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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.


Course 2 of 5 in the Deep Learning Specialization.


Syllabus


WEEK 1: Practical aspects of Deep Learning

WEEK 2: Optimization algorithms

WEEK 3: Hyperparameter tuning, Batch Normalization and Programming Frameworks



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