Eclipse Deeplearning4j
A distributed, deep learning library for Java virtual machine (JVM)
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| + | Java Support | Offers a programming library for Java virtual machine (JVM) environments. |
|---|---|---|
| + | Distributed Computing | Facilitates distributed deep learning across multiple CPUs and GPUs. |
| + | Model Import | Allows importing of Keras models, TensorFlow models, and ONNX (Open Neural Network Exchange) models, reducing the need to rebuild models from scratch. |
| + | Spark Integration | Integrates with Apache Spark for large-scale data processing and model training. |
| + | Data Management | Includes tools for data cleaning, ETL (Extract, Transform, Load) operations, preprocessing, and conversion into neural network-compatible vectors. |
| + | Deployment Options | Supports deployment via REST, Spark, or embedded environments like Android or Raspberry Pi. |
| + | Enterprise Security | Provides secure connections to enterprise environments using authentication protocols like Kerberos. |
| + | Python Interoperability | Provides interaction with the Python ecosystem through CPython bindings. |
| - | Model Compatibility | Due to native components, it may not be compatible with all systems. |
| - | Performance Overhead | Java virtual machine can introduce some performance overhead compared to natively compiled languages like Python. |
| - | Data Preprocessing | Data preprocessing tools might require more manual configuration compared to higher-level abstractions in other frameworks. |
| - | Development Pace | Updates and new features may be implemented less frequently compared to actively maintained frameworks. |
| - | Learning Curve | Requires familiarity with Java and potentially Scala/Clojure. Setting up the project involves configuring Maven dependencies which may be complex for beginners. |
System Requirements
| # | Minimum | Recommended |
|---|---|---|
| 1 | 4 GB | 8 GB |
| 2 |
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Ratings
3.835
| G2CROWD | 3.55 based on 1 reviews |
|---|---|
| PAT RESEARCH | 8.210 based on 1 reviews |
| PAT RESEARCH | 7.810 based on professional's opinion |
Developer
Written in
Java, C++, Python, JavaScript, Scala, Cuda
Initial Release
22 February 2014
License
Categories
Alternatives
Deep Learning
Machine Learning
Artificial Intelligence
Machine Learning
Artificial Intelligence
Notes
- Please read the docs here completely for proper understanding of system requirements and configuration.