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features
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support

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Description

Relying exclusively on a traditional patent search database means you are limited to keywords and their semantic connections, which often leads to searches that are imprecise, tedious, and time-consuming. In stark contrast, Ambercite Ai employs deep-learning and network algorithms to identify patents that closely resemble one or more initial patents. The results are organized by similarity, ensuring that searches are not only rapid but also effortless. By integrating your specialized knowledge with the advanced deep-learning capabilities of Ambercite Ai, you can gain a significant edge in your research process. Our technology pinpoints the patents most similar and relevant to your interests, utilizing sophisticated methods grounded in our extensive database of over 106 million patents and 175 million patent citations. This innovative approach helps you reclaim valuable time and resources that would otherwise be spent sifting through less relevant patent documents. With features such as abstracts, representative images, and intelligent review tools, you can efficiently focus on the patents that truly matter to your work, ultimately enhancing your productivity and decision-making capabilities.

Description

DL4J leverages state-of-the-art distributed computing frameworks like Apache Spark and Hadoop to enhance the speed of training processes. When utilized with multiple GPUs, its performance matches that of Caffe. Fully open-source under the Apache 2.0 license, the libraries are actively maintained by both the developer community and the Konduit team. Deeplearning4j, which is developed in Java, is compatible with any language that runs on the JVM, including Scala, Clojure, and Kotlin. The core computations are executed using C, C++, and CUDA, while Keras is designated as the Python API. Eclipse Deeplearning4j stands out as the pioneering commercial-grade, open-source, distributed deep-learning library tailored for Java and Scala applications. By integrating with Hadoop and Apache Spark, DL4J effectively introduces artificial intelligence capabilities to business settings, enabling operations on distributed CPUs and GPUs. Training a deep-learning network involves tuning numerous parameters, and we have made efforts to clarify these settings, allowing Deeplearning4j to function as a versatile DIY resource for developers using Java, Scala, Clojure, and Kotlin. With its robust framework, DL4J not only simplifies the deep learning process but also fosters innovation in machine learning across various industries.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Apache Spark No 
Google Patents Yes 
Hadoop No 

Integrations

Apache Spark Yes 
Google Patents No 
Hadoop Yes 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Ambercite

Country

Australia

Website

www.ambercite.com/ambercite-ai

Vendor Details

Company Name

Deeplearning4j

Founded

2019

Country

Japan

Website

deeplearning4j.org

Product Features

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

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