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Description

ConvNetJS is a JavaScript library designed for training deep learning models, specifically neural networks, directly in your web browser. With just a simple tab open, you can start the training process without needing any software installations, compilers, or even GPUs—it's that hassle-free. The library enables users to create and implement neural networks using JavaScript and was initially developed by @karpathy, but it has since been enhanced through community contributions, which are greatly encouraged. For those who want a quick and easy way to access the library without delving into development, you can download the minified version via the link to convnet-min.js. Alternatively, you can opt to get the latest version from GitHub, where the file you'll likely want is build/convnet-min.js, which includes the complete library. To get started, simply create a basic index.html file in a designated folder and place build/convnet-min.js in the same directory to begin experimenting with deep learning in your browser. This approach allows anyone, regardless of their technical background, to engage with neural networks effortlessly.

Description

In 2021, we launched our research-grade neural networks specifically for SEO link building, and the outcomes exceeded all expectations. With a training phase that included 15 test clients, the results were astonishingly effective. Gone are the days of struggling to come up with innovative and effective methods for constructing authoritative backlink profiles, as well as the extensive link profile audits that used to consume so much time. We now produce insights in mere seconds that previously took days or even weeks to uncover. Our neural network is capable of generating the most data-driven link-building strategies available globally, merging content partnerships with over 100,000 publishers through advanced algorithms. In less than 20 seconds, we can identify the most prestigious publications that are best suited for your needs. Previously, the concept of authority link-building was fraught with challenges, requiring significant manual effort and filled with uncertainties, such as determining who would grant authority and whether that authority was credible. With our cutting-edge AI-driven SEO technology, we have revolutionized this process, providing a reliable and efficient solution for link-building challenges. As a result, businesses can now focus more on their core operations while we handle the complexities of SEO link building.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

No images available

Integrations

Qwen3-Omni

Integrations

Qwen3-Omni

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

ConvNetJS

Website

cs.stanford.edu/people/karpathy/convnetjs/

Vendor Details

Company Name

VikingLinks

Founded

2021

Country

The Netherlands

Website

www.vikinglinks.com

Product Features

Deep Learning

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

Product Features

SEO

A/B Testing
Artificial Intelligence (AI)
Auditing
Competitor Analysis
Content Management
Dashboard
Google Analytics Integration
Keyword Research Tools
Keyword Tracking
Link Management
Localization
Mobile Search Tracking
Rank Tracking
Revenue Management
User Management

Alternatives

Alternatives