In this section, we will first provide some contextual information about RNNs. Then, we will highlight some potential drawbacks of classical RNNs. Finally, we will see an improved variation of RNNs called LSTM to address the drawbacks.
- Preface
- Analyzing Insurance Severity Claims
- Machine learning and learning workflow
- Hyperparameter tuning and cross-validation
- Analyzing and predicting insurance severity claims
- LR for predicting insurance severity claims
- GBT regressor for predicting insurance severity claims
- Boosting the performance using random forest regressor
- Comparative analysis and model deployment
- Summary
- Analyzing and Predicting Telecommunication Churn
- High Frequency Bitcoin Price Prediction from Historical and Live Data
- Population-Scale Clustering and Ethnicity Prediction
- Topic Modeling - A Better Insight into Large-Scale Texts
- Topic modeling and text clustering
- 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
- Implementation
- Other topic models versus the scalability of LDA
- Deploying the trained LDA model
- Summary
- Developing Model-based Movie Recommendation Engines
- Recommendation system
- Spark-based movie recommendation systems
- Item-based collaborative filtering for movie similarity
- 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
- Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks
- Client subscription assessment through telemarketing
- Dataset description
- Installing and getting started with Apache Zeppelin
- 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
- Summary
- Client subscription assessment through telemarketing
- Fraud Analytics Using Autoencoders and Anomaly Detection
- Outlier and anomaly detection
- Autoencoders and unsupervised learning
- 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
- Human activity recognition using the LSTM model
- 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
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Working with RNNs
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