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Ratings and Reviews 0 Ratings
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What is NeuroIntelligence?
NeuroIntelligence is a sophisticated software tool that utilizes neural networks to assist professionals in areas such as data mining, pattern recognition, and predictive modeling while addressing real-world issues. By incorporating only thoroughly validated neural network algorithms and techniques, the application guarantees both rapid performance and ease of use. Among its features are visualized architecture searches and extensive training and testing capabilities for neural networks. Users are equipped with tools such as fitness bars and training graph comparisons, allowing them to keep track of important metrics like dataset error, network error, and weight distributions. The software offers an in-depth analysis of input significance and includes testing instruments like actual versus predicted graphs, scatter plots, response graphs, ROC curves, and confusion matrices. With its user-friendly design, NeuroIntelligence effectively tackles challenges in data mining, forecasting, classification, and pattern recognition. This streamlined interface not only enhances user experience but also incorporates innovative features that save time, enabling users to create superior solutions more efficiently. As a result, users can dedicate their efforts towards refining their models and attaining improved outcomes in their projects. The ability to visualize and analyze data effectively ensures that professionals can make informed decisions based on their findings.
What is Deeplearning4j?
DL4J utilizes cutting-edge distributed computing technologies like Apache Spark and Hadoop to significantly improve training speed. When combined with multiple GPUs, it achieves performance levels that rival those of Caffe. Completely open-source and licensed under Apache 2.0, the libraries benefit from active contributions from both the developer community and the Konduit team. Developed in Java, Deeplearning4j can work seamlessly with any language that operates on the JVM, which includes Scala, Clojure, and Kotlin. The underlying computations are performed in C, C++, and CUDA, while Keras serves as the Python API. Eclipse Deeplearning4j is recognized as the first commercial-grade, open-source, distributed deep-learning library specifically designed for Java and Scala applications. By connecting with Hadoop and Apache Spark, DL4J effectively brings artificial intelligence capabilities into the business realm, enabling operations across distributed CPUs and GPUs. Training a deep-learning network requires careful tuning of numerous parameters, and efforts have been made to elucidate these configurations, making Deeplearning4j a flexible DIY tool for developers working with Java, Scala, Clojure, and Kotlin. With its powerful framework, DL4J not only streamlines the deep learning experience but also encourages advancements in machine learning across a wide range of sectors, ultimately paving the way for innovative solutions. This evolution in deep learning technology stands as a testament to the potential applications that can be harnessed in various fields.
API Availability
Has API
API Availability
Has API
Pricing Information
$497 per user
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
ALYUDA
Company Location
United States
Company Website
www.alyuda.com/product/neural-networks-software
Company Facts
Organization Name
Deeplearning4j
Date Founded
2019
Company Location
Japan
Company Website
deeplearning4j.org
Categories and Features
Categories and Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization