Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

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.

WEEK 1

Practical aspects of Deep Learning

15 videos

Graded: Practical aspects of deep learning

Graded: Initialization

Graded: Regularization

Graded: Gradient Checking

WEEK 2

Optimization algorithms

11 videos

Graded: Optimization algorithms

Graded: Optimization

WEEK 3

Hyperparameter tuning, Batch Normalization and Programming Frameworks

11 videos

Graded: Hyperparameter tuning, Batch Normalization, Programming

Frameworks

Graded: Tensorflow