Large Scale Machine Learning with Python > PACKT 원서리스트

1: First Steps to Scalability
Explaining scalability in detail
Python for large scale machine learning
Python packages
Summary

2: Scalable Learning in Scikit-learn
Out-of-core learning
Streaming data from sources
Stochastic learning
Feature management with data streams
Summary

3: Fast SVM Implementations
Datasets to experiment with on your own
Support Vector Machines
Feature selection by regularization
Including non-linearity in SGD
Hyperparameter tuning
Summary

4: Neural Networks and Deep Learning
The neural network architecture
Neural networks and regularization
Neural networks and hyperparameter optimization
Neural networks and decision boundaries
Deep learning at scale with H2O
Deep learning and unsupervised pretraining
Deep learning with theanets
Autoencoders and unsupervised learning
Summary

5: Deep Learning with TensorFlow
TensorFlow installation
Machine learning on TensorFlow with SkFlow
Keras and TensorFlow installation
Convolutional Neural Networks in TensorFlow through Keras
CNN's with an incremental approach
GPU Computing
Summary

6: Classification and Regression Trees at Scale
Bootstrap aggregation
Random forest and extremely randomized forest
Fast parameter optimization with randomized search
CART and boosting
XGBoost
Out-of-core CART with H2O
Summary

7: Unsupervised Learning at Scale
Unsupervised methods
Feature decomposition PCA
PCA with H2O
Clustering K-means
K-means with H2O
LDA
Summary

8: Distributed Environments – Hadoop and Spark
From a standalone machine to a bunch of nodes
Setting up the VM
The Hadoop ecosystem
Spark
Summary

9: Practical Machine Learning with Spark
Setting up the VM for this chapter
Sharing variables across cluster nodes
Data preprocessing in Spark
Machine learning with Spark
Summary

Appendix A: Introduction to GPUs and Theano
GPU computing
Theano parallel computing on the GPU
Installing Theano

Appendix B: Index