更新时间：2021-08-13 15:53:42
封面
Title Page
Copyright and Credits
Applied Deep Learning with Python
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Contributors
About the authors
About the reviewers
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Preface
Who this book is for
What this book covers
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Download the example code files
Conventions used
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Reviews
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 Exploration
Explore 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
Using Categorical Features for Segmentation Analysis
Create categorical fields 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 methods
Activity: Preparing to Train a Predictive Model for the Employee-Retention Problem
Training Classification Models
Introduction to Classification Algorithms
Training two-feature classification models with scikit-learn
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 problem
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 Bokeh
Activity: Exploring Data with Interactive Visualizations
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
Activity: Verifying Software Components
Exploring a Trained Neural Network
MNIST Dataset
Training a Neural Network with TensorFlow
Training a Neural Network
Testing Network Performance with Unseen Data
Activity: Exploring a Trained Neural Network
Model Architecture
Choosing the Right Model Architecture
Common Architectures
