Course details

Improving Neural Networks: Loss Function & Optimization

Improving Neural Networks: Loss Function & Optimization


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Learners can explore the concept of loss function, the different types of Loss function and their impact on neural networks, and the causes of optimization problems, in this 10-video course. Examine alternatives to optimization, the prominent optimizer algorithms and their associated properties, and the concept of learning rates in neural networks for machine learning solutions. Key concepts in this course include learning loss function and listing various types of loss function; recognizing impacts of the different types of loss function on neural networks models; and learning how to calculate loss function and score by using Python. Next, learners will learn to recognize critical causes of optimization problems and essential alternatives to optimization; recall prominent optimizer algorithms, along with their properties that can be applied for optimization; and how to perform comparative optimizer analysis using Keras. Finally, discover the relevance of learning rates in optimization and various approaches of improving learning rates; and learn the approach of finding learning rate by using RMSProp optimizer.



Expected Duration (hours)
1.1

Lesson Objectives

Improving Neural Networks: Loss Function & Optimization

  • discover the key concepts covered in this course
  • define Loss function and list the various types of loss function
  • recognize the impacts of the different types of loss function on neural networks models
  • calculate loss function and score using Python
  • recognize the critical causes of optimization problems and the essential alternatives to optimization
  • recall the prominent optimizer algorithms along with their properties that can be applied for optimization
  • demonstrate how to perform comparative optimizer analysis using Keras
  • recognize the relevance of learning rates in optimization and list the various approaches of improving learning rates
  • demonstrate the approach of finding learning rate using RMSProp optimizer
  • recall the different types of loss functions, list the prominent cause of optimization problems, and calculate loss function using Python
  • Course Number:
    it_mlinnrdj_02_enus

    Expertise Level
    Intermediate