PACKT_PUBLISHING_DEEP_LEARNING_WITH_R_TUTORIAL-OXBRiDGE

Section
Appz
Group
OXBRiDGE
Size
761,09 MB
Files
18
Date
2017-05-12

NFO

                                           
░ ░▒▒▒▓▓█████▌░░ University of OXford & University of camBRiDGE ░░▐████▓▓▒▒▒░ ░
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▐███▀ ▀███▀███▓▄███▀ ▐███ ▄███▀▐███▓ ▄████▀▐███▌███▓   ███▓████       ▓███▌
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▐████████▀████▌  ███████▌  ▄███████▌   ████████▓███▌▄▄███▓▀█████▄▄████▓███▄▄▄▄▄
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  ...is a collective term for characteristics that the two institutions share.


     ·-- PUBLISHED -----·> Packt Publishing                                   
     ·-- LECTURESHIP ---·> Deep Learning with R                               
                                                                              
                                                                              

     ·-- LECTURE DATE --·> 05-2017           
                           03-2017 {Published}

     ·-- LEVEL ---------·> [ ] Starting {Beginner|Newcomer}
                           [■] Progressing {Intermediate|Advanced}

     ·-- PERIOD in HRS--·> 04+ e
     ·-- SCALE ---------·> 17x50mb                                           

     ·-- LECTURE LINK --·> https://goo.gl/6OGfRh                     
                                                                     
                                                             
 
     
     Deep learning refers to artificial neural networks that are composed of 
     many layers. Deep learning is a powerful set of techniques for finding  
     accurate information from raw data.                                     
                                                                             
     This tutorial will teach you how to leverage deep learning to make sense
     of your raw data by exploring various hidden layers of data. Each       
     section in this course provides a clear and concise introduction of a   
     key topic, one or more example of implementations of these concepts in  
     R, and guidance for additional learning, exploration, and application of
     the skills learned therein. You will start by understanding the basics  
     of Deep Learning and Artificial neural Networks and move on to exploring
     advanced ANNÆs and RNNÆs. You will deep dive into Convolutional Neural  
     Networks and Unsupervised Learning. You will also learn about the       
     applications of Deep Learning in various fields and understand the      
     practical implementations of Scalability, HPC and Feature Engineering.  
                                                                             
     Starting out at a basic level, users will be learning how to develop and
     implement Deep Learning algorithms using R in real world scenarios.     
     

Files

PathSize
o_ppdlwrt.r0047,68 MB
o_ppdlwrt.r0147,68 MB
o_ppdlwrt.r0247,68 MB
o_ppdlwrt.r0347,68 MB
o_ppdlwrt.r0447,68 MB
o_ppdlwrt.r0547,68 MB
o_ppdlwrt.r0647,68 MB
o_ppdlwrt.r0747,68 MB
o_ppdlwrt.r0847,68 MB
o_ppdlwrt.r0947,68 MB
o_ppdlwrt.r1047,68 MB
o_ppdlwrt.r1147,68 MB
o_ppdlwrt.r1247,68 MB
o_ppdlwrt.r1347,68 MB
o_ppdlwrt.r1445,83 MB
o_ppdlwrt.rar47,68 MB
o_ppdlwrt.sfv384 B
oxbridge.nfo3,00 KB