LEADER 00000cam  2200337Ii 4500 
001    sky299312019 
003    SKY 
005    20200602135200.0 
008    190821t20202020caua          001 0 eng d 
020    9781484253489 
020    1484253485 
040    YDX|beng|erda|cYDX|dJRZ|dSKYRV|dUtOrBLW 
092    006.31|bTEN 2020 
100 1  El-Amir, Hisham,|eauthor. 
245 10 Deep learning pipeline :|bbuilding a deep learning model 
       with TensorFlow /|cHisham El-Amir, Mahmoud Hamdy 
264  1 [California] :|bApress,|c[2020] 
264  4 |c©2020 
300    xxv, 551 pages :|billustrations ;|c24 cm 
336    text|btxt|2rdacontent 
336    still image|bsti|2rdacontent 
337    unmediated|bn|2rdamedia 
338    volume|bnc|2rdacarrier 
500    Includes index 
505 00 |tPart 1. Introduction.  Gentle introduction --|tSetting 
       up your environment --|tA tour through the deep learning 
       pipeline --|tBuild your first toy TensorFlow app --|tPart 
       2. Data.  Defining data --|tData wrangling and 
       preprocessing --|tData resampling --|tFeature selection 
       and feature engineering --|tPart 3. TensorFlow.  Deep 
       learning fundamentals --|tImproving deep neural networks -
       -|tConvolutional neural network --|tSequential models --
       |tPart 4. Applying what you've learned.  Selected topics 
       in computer vision --|tSelected topics in natural language
       processing --|tApplications 
520    "Build your own pipeline based on modern TensorFlow 
       approaches rather than outdated engineering concepts.  
       This book shows you how to build a deep learning pipeline 
       for real-life TensorFlow projects.  You'll learn what a 
       pipeline is and how it works so you can build a full 
       application easily and rapidly.  Then troubleshoot and 
       overcome basic Tensorflow obstacles to easily create 
       functional apps and deploy well-trained models.  Step-by-
       step and example-oriented instructions help you understand
       each step of the deep learning pipeline while you apply 
       the most straightforward and effective tools to 
       demonstrative problems and datasets.  You'll also develop 
       a deep learning project by preparing data, choosing the 
       model that fits that data, and debugging your model to get
       the best fit to data all using Tensorflow techniques.  
       Enhance your skills by accessing some of the most powerful
       recent trends in data science.  If you've ever considered 
       building your own image or text-tagging solution or 
       entering a Kaggle contest, "Deep learning pipeline" is for
       you!"--|cProvided by publisher 
630 00 TensorFlow. 
650  0 Machine learning. 
650  0 Neural networks (Computer science) 
700 1  Hamdy, Mahmoud,|eauthor.