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Applied deep learning with Python [electronic resource] use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions
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| 자료유형 | 단행본 |
|---|---|
| 서명/저자사항 | Applied deep learning with Python : use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions/ Alex Galea, . [electronic resource]. |
| 개인저자 | Galea, Alex.author. Capelo, Luis,author, |
| 발행사항 | Birmingham, UK: Packt, [2018]. |
| 형태사항 | 1 online resource (329 p.). |
| 기타형태 저록 | Print version: Galea, Alex Applied Deep Learning with Python : Use Scikit-Learn, TensorFlow, and Keras to Create Intelligent Systems and Machine Learning Solutions Birmingham : Packt Publishing Ltd,c2018 9781789804744 |
| ISBN | 9781789806991 1789806992 |
| 일반주기 |
Description based upon print version of record.
Activity:Verifying Software Components |
| 내용주기 | 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 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 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 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 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 |
| 요약 | 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)! |
| 일반주제명 | Python (Computer program language) Machine learning. COMPUTERS --Programming Languages --Python. Machine learning. Python (Computer program language) |
| 언어 | 영어 |
| 바로가기 |  |
