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• Leave a review - let other readers know what you think
• Other Books You May Enjoy
• Summary
• Tuning and optimizing CNN hyperparameters
• Wrapping up by executing the main() method
• Evaluating the model

• Leave a review - let other readers know what you think 更新時間：2021-06-30 19:06:29
• Other Books You May Enjoy
• Summary
• Tuning and optimizing CNN hyperparameters
• Wrapping up by executing the main() method
• Evaluating the model
• Training the CNNs and saving the trained models
• Preparing the ND4j dataset
• Image feature extraction
• Extracting image metadata
• Image processing
• Implementing CNNs for image classification
• Workflow of the overall project
• Description of the image dataset
• Problem description
• Large-scale image classification using CNN
• Convolutional and subsampling operations in DL4j
• Configuring DL4j ND4s and ND4j
• Convolutional and subsampling operations in DL4j
• Subsampling operations
• Pooling layer and padding operations
• Convolutional operations
• CNN architecture
• Image classification and drawbacks of DNNs
• Image Classification using Convolutional Neural Networks
• Summary
• Tuning LSTM hyperparameters and GRU
• Step 9 - Evaluating the model
• Step 8 - Training the LSTM network
• Step 7 - Setting up an optimizer
• Step 6 - LSTM network construction
• Step 5 - Defining internal RNN structure and LSTM hyperparameters
• Step 4 - Exploratory analysis of the dataset
• Step 3 - Loading and parsing the training and test set
• Step 2 - Creating MXNet context
• Step 1 - Importing necessary libraries and packages
• Implementing an LSTM model for HAR
• Setting and configuring MXNet for Scala
• Dataset description
• Human activity recognition using the LSTM model
• LSTM networks
• RNN and the long-term dependency problem
• Contextual information and the architecture of RNNs
• Working with RNNs
• Human Activity Recognition using Recurrent Neural Networks
• Summary
• Hyperparameter tuning and feature selection
• Auxiliary classes and methods
• Step 11 - Stopping the Spark session and H2O context
• Step 10 - Model evaluation on the highly-imbalanced data
• Step 9 - Pre-trained supervised model
• Step 8 - Anomaly detection
• Step 7 - Dimensionality reduction with hidden layers
• Step 6 - Unsupervised pre-training using autoencoder
• Step 5 - Preparing the H2O DataFrame
• Step 4 - Exploratory analysis of the input data
• Step 3 - Loading and parsing input data
• Step 2 - Creating a Spark session and importing implicits
• Step 1 - Loading required packages and libraries
• Preparing programming environment
• Problem description
• Description of the dataset and using linear models
• Developing a fraud analytics model
• Efficient data representation with autoencoders
• Working principles of an autoencoder
• Autoencoders and unsupervised learning
• Outlier and anomaly detection
• Fraud Analytics Using Autoencoders and Anomaly Detection
• Summary
• Regularization
• Weight and bias initialization
• Activation functions
• Number of neurons per hidden layer
• Number of hidden layers
• Hyperparameter tuning and feature selection
• Implementing a client subscription assessment model
• Statistics of numeric features
• nr_employed distribution
• Euribor3m distribution
• cons_conf_idx distribution
• cons_price_idx features
• emp_var_rate distributions
• Previous distribution
• Pdays distribution
• Campaign distribution
• Duration distribution
• Age feature
• Previous outcome distribution
• Day distribution
• Month distribution
• Contact distribution
• Loan distribution
• Housing distribution
• Default distribution
• Education distribution
• Marital distribution
• Job distribution
• Label distribution
• Exploratory analysis of the dataset
• Creating notebooks
• Starting and stopping Apache Zeppelin
• Building from the source
• Installing and getting started with Apache Zeppelin
• Dataset description
• Client subscription assessment through telemarketing
• Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks
• Summary
• Model deployment
• Running and Deployment Instructions
• The frontend
• The backend
• Wrapping up the options trading app as a Scala web app
• Evaluating the model
• Putting it altogether
• Creating an option model
• Creating an option property
• Implementating an options trading web application
• Problem description
• Developing an options trading web app using Q-learning
• Making predictions using the trained model
• QLearning model validation
• QLearning model creation and training
• The policy and action-value
• The search space
• States and actions in QLearning
• Components of the Q-learning algorithm
• A simple Q-learning implementation
• Utility
• Policy
• Notation policy and utility in RL
• Using RL
• Reinforcement versus supervised and unsupervised learning
• Options Trading Using Q-learning and Scala Play Framework
• Summary
• Selecting and deploying the best model
• Step 8 - Evaluating the model
• Step 7 - Making predictions
• Step 6 - Build an ALS user product matrix
• Step 5 - Prepare the data for building the recommendation model using ALS
• Step 4 - Prepare training and test rating data and check the counts
• Step 3 - Explore and query for related statistics
• Step 2 - Register both DataFrames as temp tables to make querying easier
• Step 1 - Import packages load parse and explore the movie and rating dataset
• Movie recommendation using ALS
• Data exploration
• Model-based recommendation with Spark
• Step 4 - Testing the model
• Step 3 - Computing similarity
• Step 2 - Reading and parsing the dataset
• Step 1 - Importing necessary libraries and creating a Spark session
• Item-based collaborative filtering for movie similarity
• Spark-based movie recommendation systems
• The utility matrix
• Model-based collaborative filtering
• Hybrid recommender systems
• Content-based filtering approaches
• Collaborative filtering approaches
• Recommendation system
• Developing Model-based Movie Recommendation Engines
• Summary
• Deploying the trained LDA model
• Other topic models versus the scalability of LDA
• Step 8 - Measuring the likelihood of two documents
• Step 7 - Topic modelling
• Step 6 - Prepare the topics of interest
• Step 5 - Training the LDA model
• Step 4 - Set the NLP optimizer
• Step 3 - Instantiate the LDA model before training
• Step 2 - Creating vocabulary and tokens count to train the LDA after text pre-processing
• Step 1 - Creating a Spark session
• Implementation
• Topic modeling with Spark MLlib and Stanford NLP
• How does LDA algorithm work?
• Topic modeling and text clustering
• Topic Modeling - A Better Insight into Large-Scale Texts
• Summary
• Using random forest for ethnicity prediction
• Using H2O for ethnicity prediction
• Determining the number of optimal clusters
• Spark-based K-means for population-scale clustering
• Model training and hyperparameter tuning
• Data pre-processing and feature engineering
• Configuring programming environment
• DNNs for geographic ethnicity prediction
• How does K-means work?
• Population genomics and clustering
• Unsupervised machine learning
• ADAM for large-scale genomics data processing
• H2O and Sparkling water
• Algorithms tools and techniques
• 1000 Genomes Projects dataset description
• Machine learning for genetic variants
• Population scale clustering and geographic ethnicity
• Population-Scale Clustering and Ethnicity Prediction
• Summary
• Running the Scala Play web app
• Project structure
• Why RESTful architecture?
• Demo prediction using Scala Play framework
• Predicting prices and evaluating the model
• TraderActor
• PredictionActor and the prediction step
• SchedulerActor
• Scheduler
• JobModule
• Web service workflow
• Concurrency through Akka actors
• Scala Play web service
• Model training for prediction
• Real-time data through the Cryptocompare API
• Data preprocessing
• Assumptions and design choices
• Transformation of historical data into a time series
• Historical data collection
• Historical and live-price data collection
• High-level data pipeline of the prototype
• Prediction
• Training
• State-of-the-art automated trading of Bitcoin
• Bitcoin cryptocurrency and online trading
• High Frequency Bitcoin Price Prediction from Historical and Live Data
• Summary
• Selecting the best model for deployment
• Random Forest for churn prediction
• DTs for churn prediction
• SVM for churn prediction
• LR for churn prediction
• Exploratory analysis and feature engineering
• Description of the dataset
• Developing a churn analytics pipeline
• Why do we perform churn analysis and how do we do it?
• Analyzing and Predicting Telecommunication Churn
• Summary
• Spark-based model deployment for large-scale dataset
• Comparative analysis and model deployment
• Random Forest for classification and regression
• Boosting the performance using random forest regressor
• GBT regressor for predicting insurance severity claims
• Developing insurance severity claims predictive model using LR
• LR for predicting insurance severity claims
• Data preprocessing
• Exploratory analysis of the dataset
• Description of the dataset
• Motivation
• Analyzing and predicting insurance severity claims
• Hyperparameter tuning and cross-validation
• Typical machine learning workflow
• Machine learning and learning workflow
• Analyzing Insurance Severity Claims
• Reviews
• Get in touch
• Conventions used
• Download the color images
• Download the example code files
• To get the most out of this book
• What this book covers
• Who this book is for
• Preface
• Packt is searching for authors like you
• About the reviewer
• About the author
• Contributors
• PacktPub.com
• Why subscribe?
• Packt Upsell
• Title Page
• coverpage

• coverpage
• Title Page
• Packt Upsell
• Why subscribe?
• PacktPub.com
• Contributors
• About the author
• About the reviewer
• Packt is searching for authors like you
• Preface
• Who this book is for
• What this book covers
• To get the most out of this book
• Download the example code files
• Download the color images
• Conventions used
• Get in touch
• Reviews
• 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
• 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
• 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
• 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
• 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
• 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
• 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 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
• 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
• 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
• 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
• Evaluating the model
• Wrapping up by executing the main() method
• Tuning and optimizing CNN hyperparameters
• Summary
• Other Books You May Enjoy
• Leave a review - let other readers know what you think 更新時間：2021-06-30 19:06:29