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Decision Tree and Random Forest
    Machine Learning and Algorithms: The Future Is Here! (Artificial Intelligence, Book 5)
    
        By:
        












    





    





    
        
            
            
                
            
        
        William Sullivan
    
    


    
    
        Narrated by:
        












    





    





    
        
            
            
                
            
        
        Suzanne LeBlanc
    
    


    
    Length: 2 hrs and 34 mins
    9 ratings
    Overall 5.0
  • Decision Tree and Random Forest

  • Machine Learning and Algorithms: The Future Is Here! (Artificial Intelligence, Book 5)
  • By: William Sullivan
  • Narrated by: Suzanne LeBlanc
  • Length: 2 hrs and 34 mins
  • Unabridged
  • Overall
    5 out of 5 stars 9
  • Performance
    5 out of 5 stars 9
  • Story
    5 out of 5 stars 9

This audiobook installment goes over the fundamental concepts of both decision trees and random forests, but explains it to listeners in simpler terms and breaks down the complexity of the subject matter in more comprehensible components.   

  • 5 out of 5 stars
  • well explained

  • By danush on 06-07-18
  • Decision Tree and Random Forest
  • Machine Learning and Algorithms: The Future Is Here! (Artificial Intelligence, Book 5)
  • By: William Sullivan
  • Narrated by: Suzanne LeBlanc

very well written

Overall
5 out of 5 stars
Performance
5 out of 5 stars
Story
5 out of 5 stars

Reviewed: 06-04-18

They base this off straightforward things like what you normally scan for, take a gander at or collaborate with through remarking or enjoying a post. Nonetheless, I didn't know about the amount of what I do online is based around these calculations and it's fascinating to perceive how they are made and the utilization of a choice tree.

  • Convolutional Neural Networks In Python

  • Beginner's Guide to Convolutional Neural Networks in Python
  • By: Frank Millstein
  • Narrated by: Jon Wilkins
  • Length: 2 hrs and 10 mins
  • Unabridged
  • Overall
    5 out of 5 stars 40
  • Performance
    4.5 out of 5 stars 40
  • Story
    4.5 out of 5 stars 40

This audiobook covers the basics behind convolutional neural networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy-to-understand way. It is perfect for any beginner out there looking forward to learning more about this machine learning field.This audiobook is all about how to use convolutional neural networks for various image, object, and other common classification problems in Python. Here, we also take a deeper look into various Keras layer used for building CNNs.

  • 5 out of 5 stars
  • excellent book!

  • By nichu on 09-21-18

interesting

Overall
5 out of 5 stars
Performance
5 out of 5 stars
Story
5 out of 5 stars

Reviewed: 06-04-18

Great, I don’t have to get many books as this book’. I’m having a hard time with the convolutional neural network. Glad to know that the author has simplified it in every way that he can.

  • Deep Learning with Keras

  • Beginner’s Guide to Deep Learning with Keras
  • By: Frank Millstein
  • Narrated by: Jon Wilkins
  • Length: 3 hrs and 20 mins
  • Unabridged
  • Overall
    5 out of 5 stars 21
  • Performance
    4.5 out of 5 stars 21
  • Story
    4.5 out of 5 stars 21

This audiobook will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression, and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition, and much more. Furthermore, you will get familiar with recurrent neural networks like LSTM and GAN as you explore processing sequence data like time series, text, and audio.

  • 5 out of 5 stars
  • best one

  • By Anonymous User on 06-07-18

best book

Overall
5 out of 5 stars
Performance
5 out of 5 stars
Story
5 out of 5 stars

Reviewed: 06-04-18

Great, I don’t have to get many books as this books. I’m having a hard time with the convolutional neural network. Glad to know that the author has simplified it in every way that he can.