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How it works…
by Rahul Raj
Java Deep Learning Cookbook
Title Page
Copyright and Credits
Java Deep Learning Cookbook
Dedication
About Packt
Why subscribe?
Contributors
About the author
About the reviewers
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
Sections
Getting ready
How to do it…
How it works…
There's more…
See also
Get in touch
Reviews
Introduction to Deep Learning in Java
Technical requirements
Deep learning intuition
Backpropagation
Multilayer Perceptron (MLP)
Convolutional Neural Network (CNN)
Recurrent Neural Network (RNN)
Why is DL4J important for deep learning?
Determining the right network type to solve deep learning problems
How to do it...
How it works...
There's more...
Determining the right activation function
How to do it...
How it works...
There's more...
Combating overfitting problems
How to do it...
How it works...
There's more...
Determining the right batch size and learning rates
How to do it...
How it works...
There's more...
Configuring Maven for DL4J
Getting ready
How to do it...
How it works...
Configuring DL4J for a GPU-accelerated environment
Getting ready
How to do it...
How it works...
There's more...
Troubleshooting installation issues
Getting ready
How to do it...
How it works...
There's more...
Data Extraction, Transformation, and Loading
Technical requirements
Reading and iterating through data
Getting ready
How to do it...
How it works...
There's more...
Performing schema transformations
How to do it...
How it works...
There's more...
Building a transformation process
How to do it...
How it works...
There's more...
Serializing transforms
How to do it...
How it works...
Executing a transform process
How to do it...
How it works...
There's more...
Normalizing data for network efficiency
How to do it...
How it works...
There's more...
Building Deep Neural Networks for Binary Classification
Technical requirements
Extracting data from CSV input
How to do it...
How it works...
Removing anomalies from the data
How to do it...
How it works...
There's more...
Applying transformations to the data
How to do it...
How it works...
Designing input layers for the neural network model
Getting ready
How to do it...
How it works...
Designing hidden layers for the neural network model
How to do it...
How it works...
Designing output layers for the neural network model
How to do it...
How it works...
Training and evaluating the neural network model for CSV data
How to do it...
How it works...
There's more...
Deploying the neural network model and using it as an API
Getting ready
How to do it...
How it works...
Building Convolutional Neural Networks
Technical requirements
Extracting images from disk
How to do it...
How it works...
Creating image variations for training data
How to do it...
How it works...
There's more...
Image preprocessing and the design of input layers
How to do it...
How it works...
Constructing hidden layers for a CNN
How to do it...
How it works...
Constructing output layers for output classification
How to do it...
How it works...
Training images and evaluating CNN output
How to do it...
How it works...
There's more...
Creating an API endpoint for the image classifier
How to do it...
How it works...
Implementing Natural Language Processing
Technical requirements
Data requirements
Reading and loading text data
Getting ready
How to do it...
How it works...
There's more...
See also
Tokenizing data and training the model
How to do it...
How it works...
There's more...
Evaluating the model
How to do it...
How it works...
There's more...
Generating plots from the model
Getting ready
How to do it...
How it works...
Saving and reloading the model
How to do it...
How it works...
Importing Google News vectors
How to do it...
How it works...
There's more...
Troubleshooting and tuning Word2Vec models
How to do it...
How it works...
See also
Using Word2Vec for sentence classification using CNNs
Getting ready
How to do it...
How it works...
There's more...
Using Doc2Vec for document classification
How to do it...
How it works...
Constructing an LSTM Network for Time Series
Technical requirements
Extracting and reading clinical data
How to do it...
How it works...
Loading and transforming data
Getting ready
How to do it...
How it works...
Constructing input layers for the network
How to do it...
How it works...
Constructing output layers for the network
How to do it...
How it works...
Training time series data
How to do it...
How it works...
Evaluating the LSTM network's efficiency
How to do it...
How it works...
Constructing an LSTM Neural Network for Sequence Classification
Technical requirements
Extracting time series data
How to do it...
How it works...
Loading training data
How to do it...
How it works...
Normalizing training data
How to do it...
How it works...
Constructing input layers for the network
How to do it...
How it works...
Constructing output layers for the network
How to do it...
How it works...
Evaluating the LSTM network for classified output
How to do it...
How it works...
Performing Anomaly Detection on Unsupervised Data
Technical requirements
Extracting and preparing MNIST data
How to do it...
How it works...
Constructing dense layers for input
How to do it...
How it works...
Constructing output layers
How to do it...
How it works...
Training with MNIST images
How to do it...
How it works...
Evaluating and sorting the results based on the anomaly score
How to do it...
How it works...
Saving the resultant model
How to do it...
How it works...
There's more...
Using RL4J for Reinforcement Learning
Technical requirements
Setting up the Malmo environment and respective dependencies
Getting ready
How to do it...
How it works...
Setting up the data requirements
How to do it...
How it works...
See also
Configuring and training a DQN agent
Getting ready
How to do it...
How it works...
There's more...
Evaluating a Malmo agent
Getting ready
How to do it...
How it works...
Developing Applications in a Distributed Environment
Technical requirements
Setting up DL4J and the required dependencies
Getting ready
How to do it...
How it works...
Creating an uber-JAR for training
How to do it...
How it works...
CPU/GPU-specific configuration for training
How to do it...
How it works...
There's more...
Memory settings and garbage collection for Spark
How to do it...
How it works...
There's more...
Configuring encoding thresholds
How to do it...
How it works...
There's more...
Performing a distributed test set evaluation
How to do it...
How it works....
Saving and loading trained neural network models
How to do it...
How it works...
There's more...
Performing distributed inference
How to do it...
How it works...
Applying Transfer Learning to Network Models
Technical requirements
Modifying an existing customer retention model
How to do it...
How it works...
There's more...
Fine-tuning the learning configurations
How to do it...
How it works...
Implementing frozen layers
How to do it...
How it works...
Importing and loading Keras models and layers
Getting ready
How to do it...
How it works...
Benchmarking and Neural Network Optimization
Technical requirements
DL4J/ND4J-specific configuration
Getting ready
How to do it...
How it works...
There's more...
Setting up heap spaces and garbage collection
How to do it...
How it works...
There's more...
See also
Using asynchronous ETL
How to do it...
How it works...
There's more...
Using arbiter to monitor neural network behavior
How to do it...
How it works...
Performing hyperparameter tuning
How to do it...
How it works...
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