Basic knowledge on the following are prerequisites for this recipe:
- Q-learning
- DQN
Q-learning basics will help while configuring the Q-learning hyperparameters for the DQN.
- Title Page
- Copyright and Credits
- Dedication
- About Packt
- Contributors
- Preface
- Introduction to Deep Learning in Java
- Technical requirements
- Deep learning intuition
- Determining the right network type to solve deep learning problems
- Determining the right activation function
- Combating overfitting problems
- Determining the right batch size and learning rates
- Configuring Maven for DL4J
- Configuring DL4J for a GPU-accelerated environment
- Troubleshooting installation issues
- Data Extraction, Transformation, and Loading
- Building Deep Neural Networks for Binary Classification
- Technical requirements
- Extracting data from CSV input
- Removing anomalies from the data
- Applying transformations to the data
- Designing input layers for the neural network model
- Designing hidden layers for the neural network model
- Designing output layers for the neural network model
- Training and evaluating the neural network model for CSV data
- Deploying the neural network model and using it as an API
- Building Convolutional Neural Networks
- Technical requirements
- Extracting images from disk
- Creating image variations for training data
- Image preprocessing and the design of input layers
- Constructing hidden layers for a CNN
- Constructing output layers for output classification
- Training images and evaluating CNN output
- Creating an API endpoint for the image classifier
- Implementing Natural Language Processing
- Technical requirements
- Reading and loading text data
- Tokenizing data and training the model
- Evaluating the model
- Generating plots from the model
- Saving and reloading the model
- Importing Google News vectors
- Troubleshooting and tuning Word2Vec models
- Using Word2Vec for sentence classification using CNNs
- Using Doc2Vec for document classification
- Constructing an LSTM Network for Time Series
- Constructing an LSTM Neural Network for Sequence Classification
- Performing Anomaly Detection on Unsupervised Data
- Using RL4J for Reinforcement Learning
- Developing Applications in a Distributed Environment
- Technical requirements
- Setting up DL4J and the required dependencies
- Creating an uber-JAR for training
- CPU/GPU-specific configuration for training
- Memory settings and garbage collection for Spark
- Configuring encoding thresholds
- Performing a distributed test set evaluation
- Saving and loading trained neural network models
- Performing distributed inference
- Applying Transfer Learning to Network Models
- Benchmarking and Neural Network Optimization
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Getting ready
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