Deep Learning By Example

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Title Page

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Contributors

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Preface

Who this book is for

What this book covers

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Conventions used

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Data Science - A Birds' Eye View

Understanding data science by an example

Design procedure of data science algorithms

Data pre-processing

Data cleaning

Data pre-processing

Feature selection

Model selection

Learning process

Evaluating your model

Getting to learn

Challenges of learning

Feature extraction – feature engineering

Noise

Overfitting

Selection of a machine learning algorithm

Prior knowledge

Missing values

Implementing the fish recognition/detection model

Knowledge base/dataset

Data analysis pre-processing

Model building

Model training and testing

Fish recognition – all together

Different learning types

Supervised learning

Unsupervised learning

Semi-supervised learning

Reinforcement learning

Data size and industry needs

Summary

Data Modeling in Action - The Titanic Example

Linear models for regression

Motivation

Advertising – a financial example

Dependencies

Importing data with pandas

Understanding the advertising data

Data analysis and visualization

Simple regression model

Learning model coefficients

Interpreting model coefficients

Using the model for prediction

Linear models for classification

Classification and logistic regression

Titanic example – model building and training

Data handling and visualization

Data analysis – supervised machine learning

Different types of errors

Apparent (training set) error

Generalization/true error

Summary

Feature Engineering and Model Complexity – The Titanic Example Revisited

Feature engineering

Types of feature engineering

Feature selection

Dimensionality reduction

Feature construction

Titanic example revisited

Missing values

Removing any sample with missing values in it

Missing value inputting

Assigning an average value

Using a regression or another simple model to predict the values of missing variables

Feature transformations

Dummy features

Factorizing

Scaling

Binning

Derived features

Name

Cabin

Ticket

Interaction features

The curse of dimensionality

Avoiding the curse of dimensionality

Titanic example revisited – all together

Bias-variance decomposition

Learning visibility

Breaking the rule of thumb

Summary

Get Up and Running with TensorFlow