Too Many Variables
73 🏗️ Autoencoders

Feature Engineering A-Z

Preface
Introduction
Numeric Features
Categorical Features
Datetime Features
Missing Data
Text Features
Circular Features
Too Many Variables
Correlated Data
- 79 Correlated Overview
- 80 High Correlation Filter
Outliers
Imbalanced Data
Miscellaneous
Spatial
Time-Series Data
Image Data
Ralational Data
Video Data
- 138 Video Overview
- 139 🏗️ Temporary
Sound Data
- 140 Sound Overview
- 141 🏗️ Temporary
142 🏗️ Order of transformations
143 🏗️ What should you do if you have sparse data?
144 🏗️ How Different Models Deal With Input
145 🏗️ Summary
References

Table of contents

73.1 Autoencoders
73.2 Pros and Cons
- 73.2.1 Pros
- 73.2.2 Cons
73.3 R Examples
73.4 Python Examples

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Too Many Variables
73 🏗️ Autoencoders

73 🏗️ Autoencoders

73.1 Autoencoders

WIP

73.2 Pros and Cons

73.2.1 Pros

73.2.2 Cons

73.3 R Examples

73.4 Python Examples

72 🏗️ Generalized Discriminant Analysis

74 🏗️ Uniform Manifold Approximation and Projection' class='pagination-link' href='/too-many-umap'> 74 🏗️ Uniform Manifold Approximation and Projection

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