Improving Neural Networks: Loss Function & Optimization

Neural Networks    |    Intermediate
  • 10 videos | 1h 3m 45s
  • Includes Assessment
  • Earns a Badge
Rating 3.2 of 5 users Rating 3.2 of 5 users (5)
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • 1m 20s
  • 4m 19s
    In this video, you will learn how to define a loss function and list the various types of loss function. FREE ACCESS
  • Locked
    3.  Impact of Loss Function
    8m 39s
    Upon completion of this video, you will be able to recognize the impacts of different types of loss functions on neural networks models. FREE ACCESS
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    4.  Calculating Loss Function
    5m 56s
    In this video, learn how to calculate the loss function and score using Python. FREE ACCESS
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    5.  Causes of Optimization Problems
    8m 39s
    Upon completion of this video, you will be able to recognize the critical causes of optimization problems and the essential alternatives to optimization. FREE ACCESS
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    6.  Optimizer Algorithms
    9m 16s
    Upon completion of this video, you will be able to recall the prominent optimization algorithms along with their properties that can be applied for optimization. FREE ACCESS
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    7.  Comparing Optimizer Algorithms
    6m 6s
    In this video, you will learn how to perform comparative optimizer analysis using Keras. FREE ACCESS
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    8.  Learning Rate Optimizations
    5m 20s
    After completing this video, you will be able to recognize the relevance of learning rates in optimization and list the various approaches for improving learning rates. FREE ACCESS
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    9.  Implement Learning Rate Optimizer
    8m 17s
    In this video, find out how to find the learning rate using the RMSProp optimizer. FREE ACCESS
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    10.  Exercise: Working with Loss Function
    5m 53s
    Upon completion of this video, you will be able to recall the different types of loss functions, list the prominent causes of optimization problems, and calculate loss functions using Python. FREE ACCESS

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