Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Free Download. you will understand what drives performance, and be able to more systematically get good results.

Table of Contents

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Description

This course will teach you the “magic” of getting deep learning to work well. Rather than the deep learning process being a black box.

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.

Data Pipelines with TensorFlow Data Services