MARC Close

• LDR

  05542cam c2200601Ki 4500

• 001

  000000529698

• 005

  20200210174433

• 006

  m d

• 007

  cr cnu---unuuu

• 008

  180922s2018 enk o 000 0 eng d

• 015

  ▼a  GBB8H2888
  ▼2  bnb

• 016

  ▼a  019056132
  ▼2  Uk

• 019

  ▼a  1051054208

• 020

  ▼a  9781789806991
  ▼q  (electronic bk.)

• 020

  ▼a  1789806992
  ▼q  (electronic bk.)

• 035

  ▼a  1883889
  ▼b  (N$T)

• 035

  ▼a  (OCoLC)1053825266
  ▼z  (OCoLC)1051054208

• 037

  ▼a  3400A9E2-5542-4AE3-A5AD-3FBA31BD07A2
  ▼b  OverDrive, Inc.
  ▼n  http://www.overdrive.com

• 040

  ▼a  EBLCP
  ▼b  eng
  ▼e  rda
  ▼c  EBLCP
  ▼d  YDX
  ▼d  TEFOD
  ▼d  MERUC
  ▼d  IDB
  ▼d  OCLCO
  ▼d  UKMGB
  ▼d  LVT
  ▼d  OCLCF
  ▼d  N$T
  ▼d  248023

• 049

  ▼a  MAIN

• 050

  ▼a  QA76.73.P98

• 072

  ▼a  COM
  ▼x  051360
  ▼2  bisacsh

• 082

  ▼a  005.133
  ▼2  23

• 100

  ▼a  Galea, Alex.,
  ▼e  author.

• 245

  ▼a  Applied deep learning with Python:
  ▼b  use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions /:
  ▼c  Alex Galea, .
  ▼h  [electronic resource].

• 260

  ▼a  Birmingham, UK:
  ▼b  Packt,
  ▼c  [2018].

• 300

  ▼a  1 online resource (329 p.).

• 336

  ▼a  text
  ▼2  rdacontent

• 337

  ▼a  computer
  ▼2  rdamedia

• 338

  ▼a  online resource
  ▼2  rdacarrier

• 500

  ▼a  Description based upon print version of record.

• 500

  ▼a  Activity:Verifying Software Components

• 505

  ▼a  Intro; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Jupyter Fundamentals; Basic Functionality and Features; What is a Jupyter Notebook and Why is it Useful?; Navigating the Platform; Introducing Jupyter Notebooks; Jupyter Features; Exploring some of Jupyter's most useful features; Converting a Jupyter Notebook to a Python Script; Python Libraries; Import the external libraries and set up the plotting environment; Our First Analysis -- The Boston Housing Dataset; Loading the Data into Jupyter Using a Pandas DataFrame; Load the Boston housing dataset

• 505

  ▼a  Data ExplorationExplore the Boston housing dataset; Introduction to Predictive Analytics with Jupyter Notebooks; Linear models with Seaborn and scikit-learn; Activity:Building a Third-Order Polynomial Model; Linear models with Seaborn and scikit-learn; Using Categorical Features for Segmentation Analysis; Create categorical filelds from continuous variables and make segmented visualizations; Summary; Data Cleaning and Advanced Machine Learning; Preparing to Train a Predictive Model; Determining a Plan for Predictive Analytics; Preprocessing Data for Machine Learning

• 505

  ▼a  Exploring data preprocessing tools and methodsActivity:Preparing to Train a Predictive Model for the Employee-Retention Problem; Training Classification Models; Introduction to Classification Algorithms; Training two-feature classification models with scikitlearn; The plot_decision_regions Function; Training k-nearest neighbors for our model; Training a Random Forest; Assessing Models with k-Fold Cross-Validation and Validation Curves; Using k-fold cross validation and validation curves in Python with scikit-learn; Dimensionality Reduction Techniques

• 505

  ▼a  Training a predictive model for the employee retention problemSummary; Web Scraping and Interactive Visualizations; Scraping Web Page Data; Introduction to HTTP Requests; Making HTTP Requests in the Jupyter Notebook; Handling HTTP requests with Python in a Jupyter Notebook; Parsing HTML in the Jupyter Notebook; Parsing HTML with Python in a Jupyter Notebook; Activity:Web Scraping with Jupyter Notebooks; Interactive Visualizations; Building a DataFrame to Store and Organize Data; Building and merging Pandas DataFrames; Introduction to Bokeh

• 505

  ▼a  Introduction to interactive visualizations with BokehActivity:Exploring Data with Interactive Visualizations; Summary; Introduction to Neural Networks and Deep Learning; What are Neural Networks?; Successful Applications; Why Do Neural Networks Work So Well?; Representation Learning; Function Approximation; Limitations of Deep Learning; Inherent Bias and Ethical Considerations; Common Components and Operations of Neural Networks; Configuring a Deep Learning Environment; Software Components for Deep Learning; Python 3; TensorFlow; Keras; TensorBoard; Jupyter Notebooks, Pandas, and NumPy

• 520

  ▼a  Getting started with data science can be overwhelming, even for experienced developers. In this two-part, hands-on book we'll show you how to apply your existing understanding of the Python language to this new and exciting field that's full of new opportunities (and high expectations)!

• 590

  ▼a  Master record variable field(s) change: 050, 072

• 650

  ▼a  Python (Computer program language)

• 650

  ▼a  Machine learning.

• 650

  ▼a  COMPUTERS
  ▼x  Programming Languages
  ▼x  Python.
  ▼2  bisacsh

• 650

  ▼a  Machine learning.
  ▼2  fast
  ▼0  (OCoLC)fst01004795

• 650

  ▼a  Python (Computer program language)
  ▼2  fast
  ▼0  (OCoLC)fst01084736

• 655

  ▼a  Electronic books.

• 700

  ▼a  Capelo, Luis,
  ▼e  author.

• 776

  ▼i  Print version:
  ▼a  Galea, Alex
  ▼t  Applied Deep Learning with Python : Use Scikit-Learn, TensorFlow, and Keras to Create Intelligent Systems and Machine Learning Solutions.
  ▼d  Birmingham : Packt Publishing Ltd,c2018,
  ▼z  9781789804744

• 852

  ▼a  NA000000
  ▼b  00000140

• 856

  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1883889

• 938

  ▼a  EBL - Ebook Library
  ▼b  EBLB
  ▼n  EBL5507773

• 938

  ▼a  YBP Library Services
  ▼b  YANK
  ▼n  15684648

• 938

  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  1883889

• 994

  ▼a  92
  ▼b  N$T