Computational Methods for Statistics
24
Dimension Reduction and Embedding
MADS Computing
Preface
Data Engineering and Distributed Environments
1
The Data Pipeline
2
Database Fundamentals
3
SQL Basics
4
Advanced SQL
5
Using SQL from Code
6
Full Text Search
7
Cloud Computing
8
Distributed Data and Computation
9
Packaging Code
10
Project: Data Pipeline
Software for Large-Scale Data
11
Apache Spark
12
Spark Data Management
13
Machine Learning in Spark
14
Spark in the Cloud
15
REST APIs
16
Web Scraping
17
Project: Distributed Data Analysis
Computational Methods for Statistics
18
Introduction to PyTorch
19
Introduction to Neural Networks
20
Fitting Neural Networks in Practice
21
Convolutional Neural Networks
22
Regularization for Neural Networks
23
Recurrent Neural Networks
24
Dimension Reduction and Embedding
References
Appendices
A
Working with Servers
Table of contents
24.1
Autoencoders
24.2
Variational autoencoders
24.3
Word embeddings
Computational Methods for Statistics
24
Dimension Reduction and Embedding
24
Dimension Reduction and Embedding
TODO
24.1
Autoencoders
24.2
Variational autoencoders
24.3
Word embeddings
23
Recurrent Neural Networks
References