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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

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Apache Spark
Google Patents
Hadoop

Integrations

Apache Spark
Google Patents
Hadoop

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

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
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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