Applied Deep Learning with Python: Use scikit-learn, TensorFlow, and Keras to create intelligent systems 9781789806991, 9781789804744

Table of contents :
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
Linear models with Seaborn and scikit-learn
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
Summary
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
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
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
Summary
Model Architecture
Choosing the Right Model Architecture
Common Architectures
Convolutional Neural Networks
Recurrent Neural Networks
Generative Adversarial Networks
Deep Reinforcement Learning
Data Normalization
Z-score
Point-Relative Normalization
Maximum and Minimum Normalization
Structuring Your Problem
Activity: Exploring the Bitcoin Dataset and Preparing Data for Model
Using Keras as a TensorFlow Interface
Model Components
Activity: Creating a TensorFlow Model Using Keras
From Data Preparation to Modeling
Training a Neural Network
Reshaping Time-Series Data
Making Predictions
Overfitting
Activity: Assembling a Deep Learning System
Summary
Model Evaluation and Optimization
Model Evaluation
Problem Categories
Loss Functions, Accuracy, and Error Rates
Different Loss Functions, Same Architecture
Using TensorBoard
Implementing Model Evaluation Metrics
Evaluating the Bitcoin Model
Overfitting
Model Predictions
Interpreting Predictions
Activity:Creating an Active Training Environment
Hyperparameter Optimization
Layers and Nodes - Adding More Layers
Adding More Nodes
Layers and Nodes - Implementation
Epochs
Epochs - Implementation
Activation Functions
Linear (Identity)
Hyperbolic Tangent (Tanh)
Rectifid Linear Unit
Activation Functions - Implementation
Regularization Strategies
L2 Regularization
Dropout
Regularization Strategies – Implementation
Optimization Results
Activity:Optimizing a Deep Learning Model
Summary
Productization
Handling New Data
Separating Data and Model
Data Component
Model Component
Dealing with New Data
Re-Training an Old Model
Training a New Model
Activity: Dealing with New Data
Deploying a Model as a Web Application
Application Architecture and Technologies
Deploying and Using Cryptonic
Activity: Deploying a Deep Learning Application
Summary
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