Large Scale Machine Learning with Python

ویرایش: [1st ed] 
نویسندگان: Alberto Boschetti,  Luca Massaron,  Bastiaan Sjardin  
سری:  
ISBN (شابک) : 9781785888021, 1785888021 
ناشر: Packt Publishing 
سال نشر: 2016 
تعداد صفحات: 420 
زبان: English 
فرمت فایل : EPUB (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) 
حجم فایل: 6 Mb 

قیمت کتاب (تومان) : 51,000

Cover -- Copyright -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: First Steps to Scalability -- Explaining scalability in detail -- Making large scale examples -- Introducing Python -- Scale up with Python -- Scale out with Python -- Python for large scale machine learning -- Choosing between Python 2 and Python 3 -- Installing Python -- Step-by-step installation -- The installation of packages -- Package upgrades -- Scientific distributions -- Introducing Jupyter/IPython -- Python packages -- NumPy -- SciPy -- Pandas -- Scikit-learn -- The matplotlib package -- Gensim -- H2O -- XGBoost -- Theano -- TensorFlow -- The sknn library -- Theanets -- Keras -- Other useful packages to install on your system -- Summary -- Chapter 2: Scalable Learning in Scikit-learn -- Out-of-core learning -- Subsampling as a viable option -- Optimizing one instance at a time -- Building an out-of-core learning system -- Streaming data from sources -- Datasets to try the real thing yourself -- The first example - streaming the bike-sharing dataset -- Using pandas I/O tools -- Working with databases -- Paying attention to the ordering of instances -- Stochastic learning -- Batch gradient descent -- Stochastic gradient descent -- The Scikit-learn SGD implementation -- Defining SGD learning parameters -- Feature management with data streams -- Describing the target -- The hashing trick -- Other basic transformations -- Testing and validation in a stream -- Trying SGD in action -- Summary -- Chapter 3: Fast SVM Implementations -- Datasets to experiment with on your own -- The bike-sharing dataset -- The covertype dataset -- Support Vector Machines -- Hinge loss and its variants -- Understanding the Scikit-learn SVM implementation -- Pursuing nonlinear SVMs by subsampling

Achieving SVM at scale with SGD -- Feature selection by regularization -- Including non-linearity in SGD -- Trying explicit high-dimensional mappings -- Hyperparameter tuning -- Other alternatives for SVM fast learning -- Nonlinear and faster with Vowpal Wabbit -- Installing VW -- Understanding the VW data format -- Python integration -- A few examples using reductions for SVM and neural nets -- Faster bike-sharing -- The covertype dataset crunched by VW -- Summary -- Chapter 4: Neural Networks and Deep Learning -- The neural network architecture -- What and how neural networks learn -- Choosing the right architecture -- The input layer -- The hidden layer -- The output layer -- Neural networks in action -- Parallelization for sknn -- Neural networks and regularization -- Neural networks and hyperparameter optimization -- Neural networks and decision boundaries -- Deep learning at scale with H2O -- Large scale deep learning with H2O -- Gridsearch on H2O -- Deep learning and unsupervised pretraining -- Deep learning with theanets -- Autoencoders and unsupervised learning -- Autoencoders -- Summary -- Chapter 5: Deep Learning with TensorFlow -- TensorFlow installation -- TensorFlow operations -- GPU computing -- Linear regression with SGD -- A neural network from scratch in TensorFlow -- Machine learning on TensorFlow with SkFlow -- Deep learning with large files - incremental learning -- Keras and TensorFlow installation -- Convolutional Neural Networks in TensorFlow through Keras -- The convolution layer -- The pooling layer -- The fully connected layer -- CNN's with an incremental approach -- GPU Computing -- Summary -- Chapter 6: Classification and Regression Trees at Scale -- Bootstrap aggregation -- Random forest and extremely randomized forest -- Fast parameter optimization with randomized search -- Extremely randomized trees and large datasets

CART and boosting -- Gradient Boosting Machines -- max_depth -- learning_rate -- Subsample -- Faster GBM with warm_start -- Training and storing GBM models -- XGBoost -- XGBoost regression -- XGBoost and variable importance -- XGBoost streaming large datasets -- XGBoost model persistence -- Out-of-core CART with H2O -- Random forest and gridsearch on H2O -- Stochastic gradient boosting and gridsearch on H2O -- Summary -- Chapter 7: Unsupervised Learning at Scale -- Unsupervised methods -- Feature decomposition - PCA -- Randomized PCA -- Incremental PCA -- Sparse PCA -- PCA with H2O -- Clustering - K-means -- Initialization methods -- K-means assumptions -- Selection of the best K -- Scaling K-means - mini-batch -- K-means with H2O -- LDA -- Scaling LDA - memory, CPUs, and machines -- Summary -- Chapter 8: Distributed Environments - Hadoop and Spark -- From a standalone machine to a bunch of nodes -- Why do we need a distributed framework? -- Setting up the VM -- VirtualBox -- Vagrant -- Using the VM -- The Hadoop ecosystem -- Architecture -- HDFS -- MapReduce -- YARN -- Spark -- pySpark -- Summary -- Chapter 9: Practical Machine Learning with Spark -- Setting up the VM for this chapter -- Sharing variables across cluster nodes -- Broadcast read-only variables -- Accumulators write-only variables -- Broadcast and accumulators together - an example -- Data preprocessing in Spark -- JSON files and Spark DataFrames -- Dealing with missing data -- Grouping and creating tables in-memory -- Writing the preprocessed DataFrame or RDD to disk -- Working with Spark DataFrames -- Machine learning with Spark -- Spark on the KDD99 dataset -- Reading the dataset -- Feature engineering -- Training a learner -- Evaluating a learner's performance -- The power of the ML pipeline -- Manual tuning -- Cross-validation -- Final cleanup -- Summary

Appendix: Introduction to GPUs and Theano -- GPU computing -- Theano - parallel computing on the GPU -- Installing Theano -- Index  Read more...
Abstract: Cover -- Copyright -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: First Steps to Scalability -- Explaining scalability in detail -- Making large scale examples -- Introducing Python -- Scale up with Python -- Scale out with Python -- Python for large scale machine learning -- Choosing between Python 2 and Python 3 -- Installing Python -- Step-by-step installation -- The installation of packages -- Package upgrades -- Scientific distributions -- Introducing Jupyter/IPython -- Python packages -- NumPy -- SciPy -- Pandas -- Scikit-learn -- The matplotlib package -- Gensim -- H2O -- XGBoost -- Theano -- TensorFlow -- The sknn library -- Theanets -- Keras -- Other useful packages to install on your system -- Summary -- Chapter 2: Scalable Learning in Scikit-learn -- Out-of-core learning -- Subsampling as a viable option -- Optimizing one instance at a time -- Building an out-of-core learning system -- Streaming data from sources -- Datasets to try the real thing yourself -- The first example - streaming the bike-sharing dataset -- Using pandas I/O tools -- Working with databases -- Paying attention to the ordering of instances -- Stochastic learning -- Batch gradient descent -- Stochastic gradient descent -- The Scikit-learn SGD implementation -- Defining SGD learning parameters -- Feature management with data streams -- Describing the target -- The hashing trick -- Other basic transformations -- Testing and validation in a stream -- Trying SGD in action -- Summary -- Chapter 3: Fast SVM Implementations -- Datasets to experiment with on your own -- The bike-sharing dataset -- The covertype dataset -- Support Vector Machines -- Hinge loss and its variants -- Understanding the Scikit-learn SVM implementation -- Pursuing nonlinear SVMs by subsampling

Achieving SVM at scale with SGD -- Feature selection by regularization -- Including non-linearity in SGD -- Trying explicit high-dimensional mappings -- Hyperparameter tuning -- Other alternatives for SVM fast learning -- Nonlinear and faster with Vowpal Wabbit -- Installing VW -- Understanding the VW data format -- Python integration -- A few examples using reductions for SVM and neural nets -- Faster bike-sharing -- The covertype dataset crunched by VW -- Summary -- Chapter 4: Neural Networks and Deep Learning -- The neural network architecture -- What and how neural networks learn -- Choosing the right architecture -- The input layer -- The hidden layer -- The output layer -- Neural networks in action -- Parallelization for sknn -- Neural networks and regularization -- Neural networks and hyperparameter optimization -- Neural networks and decision boundaries -- Deep learning at scale with H2O -- Large scale deep learning with H2O -- Gridsearch on H2O -- Deep learning and unsupervised pretraining -- Deep learning with theanets -- Autoencoders and unsupervised learning -- Autoencoders -- Summary -- Chapter 5: Deep Learning with TensorFlow -- TensorFlow installation -- TensorFlow operations -- GPU computing -- Linear regression with SGD -- A neural network from scratch in TensorFlow -- Machine learning on TensorFlow with SkFlow -- Deep learning with large files - incremental learning -- Keras and TensorFlow installation -- Convolutional Neural Networks in TensorFlow through Keras -- The convolution layer -- The pooling layer -- The fully connected layer -- CNN's with an incremental approach -- GPU Computing -- Summary -- Chapter 6: Classification and Regression Trees at Scale -- Bootstrap aggregation -- Random forest and extremely randomized forest -- Fast parameter optimization with randomized search -- Extremely randomized trees and large datasets

CART and boosting -- Gradient Boosting Machines -- max_depth -- learning_rate -- Subsample -- Faster GBM with warm_start -- Training and storing GBM models -- XGBoost -- XGBoost regression -- XGBoost and variable importance -- XGBoost streaming large datasets -- XGBoost model persistence -- Out-of-core CART with H2O -- Random forest and gridsearch on H2O -- Stochastic gradient boosting and gridsearch on H2O -- Summary -- Chapter 7: Unsupervised Learning at Scale -- Unsupervised methods -- Feature decomposition - PCA -- Randomized PCA -- Incremental PCA -- Sparse PCA -- PCA with H2O -- Clustering - K-means -- Initialization methods -- K-means assumptions -- Selection of the best K -- Scaling K-means - mini-batch -- K-means with H2O -- LDA -- Scaling LDA - memory, CPUs, and machines -- Summary -- Chapter 8: Distributed Environments - Hadoop and Spark -- From a standalone machine to a bunch of nodes -- Why do we need a distributed framework? -- Setting up the VM -- VirtualBox -- Vagrant -- Using the VM -- The Hadoop ecosystem -- Architecture -- HDFS -- MapReduce -- YARN -- Spark -- pySpark -- Summary -- Chapter 9: Practical Machine Learning with Spark -- Setting up the VM for this chapter -- Sharing variables across cluster nodes -- Broadcast read-only variables -- Accumulators write-only variables -- Broadcast and accumulators together - an example -- Data preprocessing in Spark -- JSON files and Spark DataFrames -- Dealing with missing data -- Grouping and creating tables in-memory -- Writing the preprocessed DataFrame or RDD to disk -- Working with Spark DataFrames -- Machine learning with Spark -- Spark on the KDD99 dataset -- Reading the dataset -- Feature engineering -- Training a learner -- Evaluating a learner's performance -- The power of the ML pipeline -- Manual tuning -- Cross-validation -- Final cleanup -- Summary

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