Deeplearning4j
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EN 1.0.0-beta7
EN 1.0.0-beta7
  • Eclipse DeepLearning4J
  • Getting Started
    • Quickstart
      • Untitled
    • Tutorials
      • Quickstart with MNIST
      • MultiLayerNetwork And ComputationGraph
      • Logistic Regression
      • Built-in Data Iterators
      • Feed Forward Networks
      • Basic Autoencoder
      • Advanced Autoencoder
      • Convolutional Networks
      • Recurrent Networks
      • Early Stopping
      • Layers and Preprocessors
      • Hyperparameter Optimization
      • Using Multiple GPUs
      • Clinical Time Series LSTM
      • Sea Temperature Convolutional LSTM
      • Sea Temperature Convolutional LSTM 2
      • Instacart Multitask Example
      • Instacart Single Task Example
      • Cloud Detection Example
    • Core Concepts
    • Cheat Sheet
    • Examples Tour
    • Deep Learning Beginners
    • Build from Source
    • Contribute
      • Eclipse Contributors
    • Benchmark Guide
    • About
    • Release Notes
  • Configuration
    • Backends
      • CPU and AVX
      • cuDNN
      • Performance Issues
    • Memory Management
      • Memory Workspaces
    • Snapshots
    • Maven
    • SBT, Gradle, & Others
  • Models
    • Autoencoders
    • Multilayer Network
    • Computation Graph
    • Convolutional Neural Network
    • Recurrent Neural Network
    • Layers
    • Vertices
    • Iterators
    • Listeners
    • Custom Layers
    • Model Persistence
    • Activations
    • Updaters
  • Model Zoo
    • Overview
    • Zoo Models
  • ND4J
    • Overview
    • Quickstart
    • Basics
    • Elementwise Operations
    • Matrix Manipulation
    • Syntax
    • Tensors
  • SAMEDIFF
    • Importing TensorFlow models
    • Variables
    • Ops
    • Adding Ops
  • ND4J & SameDiff Ops
    • Overview
    • Bitwise
    • Linalg
    • Math
    • Random
    • BaseOps
    • CNN
    • Image
    • Loss
    • NN
    • RNN
  • Tuning & Training
    • Evaluation
    • Visualization
    • Trouble Shooting
    • Early Stopping
    • t-SNE Visualization
    • Transfer Learning
  • Keras Import
    • Overview
    • Get Started
    • Supported Features
      • Activations
      • Losses
      • Regularizers
      • Initializers
      • Constraints
      • Optimizers
    • Functional Model
    • Sequential Model
    • Custom Layers
    • API Reference
      • Core Layers
      • Convolutional Layers
      • Embedding Layers
      • Local Layers
      • Noise Layers
      • Normalization Layers
      • Pooling Layers
      • Recurrent Layers
      • Wrapper Layers
      • Advanced Activations
  • DISTRIBUTED DEEP LEARNING
    • Introduction/Getting Started
    • Technical Explanation
    • Spark Guide
    • Spark Data Pipelines Guide
    • API Reference
    • Parameter Server
  • Arbiter
    • Overview
    • Layer Spaces
    • Parameter Spaces
  • Datavec
    • Overview
    • Records
    • Reductions
    • Schema
    • Serialization
    • Transforms
    • Analysis
    • Readers
    • Conditions
    • Executors
    • Filters
    • Operations
    • Normalization
    • Visualization
  • Language Processing
    • Overview
    • Word2Vec
    • Doc2Vec
    • Sentence Iteration
    • Tokenization
    • Vocabulary Cache
  • Mobile (Android)
    • Setup
    • Tutorial: First Steps
    • Tutorial: Classifier
    • Tutorial: Image Classifier
    • FAQ
    • Press
    • Support
    • Why Deep Learning?
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  • Why Deeplearning4j?
  • What's the use case for AI and machine learning?
  • How can I contribute?
  • Is DL4J parallelized and multi-threaded?

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FAQ

Commonly asked questions about Eclipse Deeplearning4j, deep learning, and artificial intelligence.

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Last updated 5 years ago

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With a versatile n-dimensional array class for Java and Scala, DL4J is Scalable on Hadoop, utlizes GPU support for scaling on AWS, includes a general vectorization tool for machine-learning libs, and most of all relies on ND4J: A matrix library much faster than Numpy and largely written in C++. We also built RL4J: Reinforcement Learning for Java with Deep Q learning and A3C.

AI tools like Deeplearning4j can be applied to robotic process automation (RPA), Fraud detection, network intrusion detection, Recommender Systems (CRM, adtech, churn prevention), Regression and predictive analytics, Face/image recognition, Voice search, Speech-to-text (transcription), and preventative hardware monitoring (anomaly detection).

Developers who would like to contribute to Deeplearning4j can get started by reading our .

Deeplearning4j includes both a distributed, multi-threaded deep-learning framework and a normal single-threaded deep-learning framework. Training takes place in the cluster, which means it can process massive amounts of data quickly. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala, Clojure and Kotlin. Deeplearning4j's role as a modular component in an open stack makes it the first deep-learning framework adapted for a micro-service architecture.

Contribtor's Guide
Why Deeplearning4j?
What's the use case for AI and machine learning?
How can I contribute?
Is DL4J parallelized and multi-threaded?