| LDR | | 05542cmm u2200601Ki 4500 |
| 001 | | 000000315437 |
| 003 | | OCoLC |
| 005 | | 20230525170702 |
| 006 | | m d |
| 007 | | cr cnu---unuuu |
| 008 | | 180922s2018 enk o 000 0 eng d |
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▼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 248032 |
| 049 | |
▼a MAIN |
| 050 | 4 |
▼a QA76.73.P98 |
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▼a COM
▼x 051360
▼2 bisacsh |
| 082 | 04 |
▼a 005.133
▼2 23 |
| 100 | 1 |
▼a Galea, Alex.,
▼e author. |
| 245 | 10 |
▼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 | 1 |
▼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 | 0 |
▼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 | 8 |
▼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 | 8 |
▼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 | 8 |
▼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 | 8 |
▼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 | 0 |
▼a Python (Computer program language) |
| 650 | 0 |
▼a Machine learning. |
| 650 | 7 |
▼a COMPUTERS
▼x Programming Languages
▼x Python.
▼2 bisacsh |
| 650 | 7 |
▼a Machine learning.
▼2 fast
▼0 (OCoLC)fst01004795 |
| 650 | 7 |
▼a Python (Computer program language)
▼2 fast
▼0 (OCoLC)fst01084736 |
| 655 | 4 |
▼a Electronic books. |
| 700 | 1 |
▼a Capelo, Luis,
▼e author. |
| 776 | 08 |
▼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 |
| 856 | 40 |
▼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 |
| 990 | |
▼a 관리자 |
| 994 | |
▼a 92
▼b N$T |