PluralSight.Introduction.to.TensorFlow.Bookware-KNiSO

Section

Appz

Group

KNiSO

Size

271,09 MB

Files

21

Date

2020-08-14

NFO

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                                                                             19
                              PROUDLY PRESENTING:
                    
                          Introduction.to.TensorFlow


				 INFORMATION:

  Date............: 2020-08-14
  Rars............: 19 Rar Files
  Course Length...: 1 hrs 59 mins
  Website.........: https://tinyurl.com/y5awk6v8
  Updated.........: Aug 13, 2020


			      Release Notes...:
                                                                              
  This course is focused on using the flexibility and ôease of useö of TensorFlow 
  2.x and Keras to build, train, and deploy machine learning models. You will 
  learn about the TensorFlow 2.x API hierarchy and will get to know the main components 
  of TensorFlow through hands-on exercises. We will introduce you to working with 
  datasets and feature columns. You will learn how to design and build a TensorFlow 
  2.x input data pipeline. You will get hands-on practice loading csv data, numPy 
  arrays, text data, and images using tf.Data.Dataset. You will also get hands-on 
  practice creating numeric, categorical, bucketized, and hashed feature columns. We 
  will introduce you to the Keras Sequential API and the Keras Functional API to 
  show you how to create deep learning models. WeÆll talk about activation functions, 
  loss, and optimization. Our Jupyter Notebooks hands-on labs offer you the opportunity 
  to build basic linear regression, basic logistic regression, and advanced logistic 
  regression machine learning models. You will learn how to train, deploy, and 
  productionalize machine learning models at scale with Cloud AI Platform.
  
			      Install Notes...: 
			 Unrar, Learn and Enjoy!                                                                            
                                                     
                         
			      GREETINGS:

 - KNOWN - HONOR - SKIDROW - DARKSiDERS - DAUDiO - JAVSiDERS - dbOOk - z0ne -
        

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