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| Autor principal: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing
2018.
|
| Colección: | EBSCO Academic eBook Collection Complete.
|
| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b40758679*spi |
Tabla de Contenidos:
- Cover
- Copyright and Credits
- Packt Upsell
- Contributors
- Table of Contents
- Preface
- Chapter 1: Analyzing Insurance Severity Claims
- Machine learning and learning workflow
- Typical machine learning workflow
- Hyperparameter tuning and cross-validation
- Analyzing and predicting insurance severity claims
- Motivation
- Description of the dataset
- Exploratory analysis of the dataset
- Data preprocessing
- LR for predicting insurance severity claims
- Developing insurance severity claims predictive model using LR
- GBT regressor for predicting insurance severity claims
- Boosting the performance using random forest regressor
- Random Forest for classification and regression
- Comparative analysis and model deployment
- Spark-based model deployment for large-scale dataset
- Summary
- Chapter 2: Analyzing and Predicting Telecommunication Churn
- Why do we perform churn analysis, and how do we do it?
- Developing a churn analytics pipeline
- Description of the dataset
- Exploratory analysis and feature engineering
- LR for churn prediction
- SVM for churn prediction
- DTs for churn prediction
- Random Forest for churn prediction
- Selecting the best model for deployment
- Summary
- Chapter 3: High Frequency Bitcoin Price Prediction from Historical and Live Data
- Bitcoin, cryptocurrency, and online trading
- State-of-the-art automated trading of Bitcoin
- Training
- Prediction
- High-level data pipeline of the prototype
- Historical and live-price data collection
- Historical data collection
- Transformation of historical data into a time series
- Assumptions and design choices
- Data preprocessing
- Real-time data through the Cryptocompare API
- Model training for prediction
- Scala Play web service
- Concurrency through Akka actors
- Web service workflow
- JobModule
- Scheduler.
- SchedulerActor
- PredictionActor and the prediction step
- TraderActor
- Predicting prices and evaluating the model
- Demo prediction using Scala Play framework
- Why RESTful architecture?
- Project structure
- Running the Scala Play web app
- Summary
- Chapter 4: Population-Scale Clustering and Ethnicity Prediction
- Population scale clustering and geographic ethnicity
- Machine learning for genetic variants
- 1000 Genomes Projects dataset description
- Algorithms, tools, and techniques
- H2O and Sparkling water
- ADAM for large-scale genomics data processing
- Unsupervised machine learning
- Population genomics and clustering
- How does K-means work?
- DNNs for geographic ethnicity prediction
- Configuring programming environment
- Data pre-processing and feature engineering
- Model training and hyperparameter tuning
- Spark-based K-means for population-scale clustering
- Determining the number of optimal clusters
- Using H2O for ethnicity prediction
- Using random forest for ethnicity prediction
- Summary
- Chapter 5: Topic Modeling
- A Better Insight into Large-Scale Texts
- Topic modeling and text clustering
- How does LDA algorithm work?
- Topic modeling with Spark MLlib and Stanford NLP
- Implementation
- Step 1
- Creating a Spark session
- Step 2
- Creating vocabulary and tokens count to train the LDA after text pre-processing
- Step 3
- Instantiate the LDA model before training
- Step 4
- Set the NLP optimizer
- Step 5
- Training the LDA model
- Step 6
- Prepare the topics of interest
- Step 7
- Topic modelling
- Step 8
- Measuring the likelihood of two documents
- Other topic models versus the scalability of LDA
- Deploying the trained LDA model
- Summary
- Chapter 6: Developing Model-based Movie Recommendation Engines
- Recommendation system
- Collaborative filtering approaches.
- Content-based filtering approaches
- Hybrid recommender systems
- Model-based collaborative filtering
- The utility matrix
- Spark-based movie recommendation systems
- Item-based collaborative filtering for movie similarity
- Step 1
- Importing necessary libraries and creating a Spark session
- Step 2
- Reading and parsing the dataset
- Step 3
- Computing similarity
- Step 4
- Testing the model
- Model-based recommendation with Spark
- Data exploration
- Movie recommendation using ALS
- Step 1
- Import packages, load, parse, and explore the movie and rating dataset
- Step 2
- Register both DataFrames as temp tables to make querying easier
- Step 3
- Explore and query for related statistics
- Step 4
- Prepare training and test rating data and check the counts
- Step 5
- Prepare the data for building the recommendation model using ALS
- Step 6
- Build an ALS user product matrix
- Step 7
- Making predictions
- Step 8
- Evaluating the model
- Selecting and deploying the best model
- Summary
- Chapter 7: Options Trading Using Q-learning and Scala Play Framework
- Reinforcement versus supervised and unsupervised learning
- Using RL
- Notation, policy, and utility in RL
- Policy
- Utility
- A simple Q-learning implementation
- Components of the Q-learning algorithm
- States and actions in QLearning
- The search space
- The policy and action-value
- QLearning model creation and training
- QLearning model validation
- Making predictions using the trained model
- Developing an options trading web app using Q-learning
- Problem description
- Implementating an options trading web application
- Creating an option property
- Creating an option model
- Putting it altogether
- Evaluating the model
- Wrapping up the options trading app as a Scala web app
- The backend
- The frontend
- Running and Deployment Instructions.
- Model deployment
- Summary
- Clients Chapter 8: Subscription Assessment for Bank Telemarketing using Deep Neural Networks
- Client subscription assessment through telemarketing
- Dataset description
- Installing and getting started with Apache Zeppelin
- Building from the source
- Starting and stopping Apache Zeppelin
- Creating notebooks
- Exploratory analysis of the dataset
- Label distribution
- Job distribution
- Marital distribution
- Education distribution
- Default distribution
- Housing distribution
- Loan distribution
- Contact distribution
- Month distribution
- Day distribution
- Previous outcome distribution
- Age feature
- Duration distribution
- Campaign distribution
- Pdays distribution
- Previous distribution
- emp_var_rate distributions
- cons_price_idx features
- cons_conf_idx distribution
- Euribor3m distribution
- nr_employed distribution
- Statistics of numeric features
- Implementing a client subscription assessment model
- Hyperparameter tuning and feature selection
- Number of hidden layers
- Number of neurons per hidden layer
- Activation functions
- Weight and bias initialization
- Regularization
- Summary
- Chapter 9: Fraud Analytics Using Autoencoders and Anomaly Detection
- Outlier and anomaly detection
- Autoencoders and unsupervised learning
- Working principles of an autoencoder
- Efficient data representation with autoencoders
- Developing a fraud analytics model
- Description of the dataset and using linear models
- Problem description
- Preparing programming environment
- Step 1
- Loading required packages and libraries
- Step 2
- Creating a Spark session and importing implicits
- Step 3
- Loading and parsing input data
- Step 4
- Exploratory analysis of the input data
- Step 5
- Preparing the H2O DataFrame
- Step 6
- Unsupervised pre-training using autoencoder.
- Step 7
- Dimensionality reduction with hidden layers
- Step 8
- Anomaly detection
- Step 9
- Pre-trained supervised model
- Step 10
- Model evaluation on the highly-imbalanced data
- Step 11
- Stopping the Spark session and H2O context
- Auxiliary classes and methods
- Hyperparameter tuning and feature selection
- Summary
- Chapter 10: Human Activity Recognition using Recurrent Neural Networks
- Working with RNNs
- Contextual information and the architecture of RNNs
- RNN and the long-term dependency problem
- LSTM networks
- Human activity recognition using the LSTM model
- Dataset description
- Setting and configuring MXNet for Scala
- Implementing an LSTM model for HAR
- Step 1
- Importing necessary libraries and packages
- Step 2
- Creating MXNet context
- Step 3
- Loading and parsing the training and test set
- Step 4
- Exploratory analysis of the dataset
- Step 5
- Defining internal RNN structure and LSTM hyperparameters
- Step 6
- LSTM network construction
- Step 7
- Setting up an optimizer
- Step 8
- Training the LSTM network
- Step 9
- Evaluating the model
- Tuning LSTM hyperparameters and GRU
- Summary
- Chapter 11: Image Classification using Convolutional Neural Networks
- Image classification and drawbacks of DNNs
- CNN architecture
- Convolutional operations
- Pooling layer and padding operations
- Subsampling operations
- Convolutional and subsampling operations in DL4j
- Configuring DL4j, ND4s, and ND4j
- Convolutional and subsampling operations in DL4j
- Large-scale image classification using CNN
- Problem description
- Description of the image dataset
- Workflow of the overall project
- Implementing CNNs for image classification
- Image processing
- Extracting image metadata
- Image feature extraction
- Preparing the ND4j dataset
- Training the CNNs and saving the trained models.
