Improving Neural Networks: Data Scaling & Regularization


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore how to create and optimize machine learning neural network models, scaling data, batch normalization, and internal covariate shift. Learners will discover the learning rate adaptation schedule, batch normalization, and using L1 and L2 regularization to manage overfitting problems. Key concepts covered in this 10-video course include the approach of creating deep learning network models, along with steps involved in optimizing networks, including deciding size and budget; how to implement the learning rate adaptation schedule in Keras by using SGD and specifying learning rate, epoch, and decay using Google Colab; and scaling data and the prominent data scaling methods, including data normalization and data standardization. Next, you will learn the concept of batch normalization and internal covariate shift; how to implement batch normalization using Python and TensorFlow; and the steps to implement L1 and L2 regularization to manage overfitting problems. Finally, observe how to implement gradient descent by using Python and the steps related to library import and data creation.



Expected Duration (hours)
1.6

Lesson Objectives

Improving Neural Networks: Data Scaling & Regularization

  • discover the key concepts covered in this course
  • describe the approach of creating deep learning network models along with the steps involved in optimizing the networks
  • implement the learning rate adaptation schedule in┬áKeras using SGD and specifying learning rate, epoch and decay
  • describe the concept of scaling data and list the prominent data scaling methods
  • describe the concept of batch normalization and internal covariate shift
  • demonstrate how to implement batch normalization using Python and TensorFlow
  • implement L1 regularization to manage overfitting problems
  • implement L2 regularization to manage overfitting problems
  • demonstrate how to implement gradient descent using Python
  • recall the prominent data scaling methods, implement L1 regularization and gradient descent using Python
  • Course Number:
    it_mlinnrdj_03_enus

    Expertise Level
    Intermediate