ConvNetJS
ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in a browser.
Some of the top features or benefits of ConvNetJS are: Ease of Use, Interactive Demos, Visualization, and No Dependencies. You can visit the info page to learn more.
ConvNetJS Alternatives & Competitors
The best ConvNetJS alternatives based on verified products, community votes, reviews and other factors.
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Open-Source Alternatives.
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Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Key Keras features:
User-Friendly Modularity Pre-trained Models Integration with TensorFlow
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TFlearn is a modular and transparent deep learning library built on top of Tensorflow.
Key TFlearn features:
User-Friendly Interface Modular Design Integration with TensorFlow Pre-built Models
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Consent-Based Identification of the Person Visiting Your Website Including First Name, Last Name, Email & 37 Other Data Points. Identify and Influence Your Engaged Website Visitors into Sales-Ready Leads – Before You Commit a Single Working Hour.
Key VisualVisitor features:
AI Sales Rep WebID +Person (B2C) WebID +Employee (B2B) Who To Contact - Contact Database (B2B)
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The World's AI.
Key Clarifai features:
API Artificial Intelligence Workflow Management Workflow Automation
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Machine Learning.
Key Microsoft Cognitive Toolkit (Formerly CNTK) features:
Efficiency Scalability Flexibility Seamless Integration
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Knet is a deep learning framework that supports GPU operation and automatic differentiation using dynamic computational graphs for models.
Key Knet features:
Efficiency Flexibility Julia Integration Community and Support
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Merlin is a deep learning framework written in Julia, it aims to provide a fast, flexible and compact deep learning library for machine learning.
Key Merlin features:
Julia Language Integration Composable Machine Learning Models Interoperability Community Support
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DeepPy is a MIT licensed deep learning framework that tries to add a touch of zen to deep learning as it allows for Pythonic programming.
Key DeepPy features:
Ease of Use Python Integration Lightweight
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Chainer is a flexible and intuitive framework for Neural Networks.
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Swift Brain is a neural network / machine learning library written in Swift for AI algorithms.
Key Swift Brain features:
Ease of Use Integration with Swift Lightweight Open Source
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See sharks everywhere with this AR app 🦈.
Key SHARK features:
Versatility Modular Design Performance Open Source
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Artificial intelligence and machine learning library written in Swift.
Key Swift AI features:
Native Swift Integration Open Source Performance Optimizations Community Support
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BrainCore is a simple but fast neural network framework written in Swift.
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MLKit is a simple machine learning framework written in Swift.
Key MLKit features:
Feature-Rich Ease of Integration Regular Updates Open-Source
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