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  5. LiveFyre vs TensorFlow vs rasa NLU

LiveFyre vs TensorFlow vs rasa NLU

OverviewDecisionsComparisonAlternatives

Overview

LiveFyre
LiveFyre
Stacks37
Followers14
Votes0
TensorFlow
TensorFlow
Stacks3.9K
Followers3.5K
Votes106
GitHub Stars192.3K
Forks74.9K
rasa NLU
rasa NLU
Stacks120
Followers282
Votes25

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Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

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Advice on LiveFyre, TensorFlow, rasa NLU

Xi
Xi

Developer at DCSIL

Oct 11, 2020

Decided

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

99.4k views99.4k
Comments
Adithya
Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k
Comments
philippe
philippe

Research & Technology & Innovation | Software & Data & Cloud | Professor in Computer Science

Sep 13, 2020

Review

Hello Amina, You need first to clearly identify the input data type (e.g. temporal data or not? seasonality or not?) and the analysis type (e.g., time series?, categories?, etc.). If you can answer these questions, that would be easier to help you identify the right tools (or Python libraries). If time series and Python, you have choice between Pendas/Statsmodels/Serima(x) (if seasonality) or deep learning techniques with Keras.

Good work, Philippe

4.65k views4.65k
Comments

Detailed Comparison

LiveFyre
LiveFyre
TensorFlow
TensorFlow
rasa NLU
rasa NLU

Livefyre’s real-time apps get your audience talking and turn your site into the hub for your community. Bloggers, brands and the largest publishers in the world use Livefyre to engage their users and curate live content from around the social web.

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.

SocialSync- With Livefyre SocialSync, the conversation happening on Facebook and Twitter automatically syncs directly to your content, where it belongs.;Social Sign In- Users have the option to sign in with multiple social networks to put a face to their name and comments.;Friend Tagging- Getting your friends in on the conversation is as simple as typing the “@” sign. Easily invite Facebook and Twitter friends to join the conversation right from the comment box.;Comment Sharing- With one click, users can easily share comments with their friends on other social networks including Facebook, Twitter and LinkedIn.;Comment Liking- Livefyre even shows the avatars of every user who’s liked a comment.;Rich Text Editor- bold italics, or create a bulleted list to drive home your argument.;LinkBack- LinkBack encourages community interaction by letting other Livefyre bloggers display a link to their latest post when they leave a comment.;Live Listeners- lets readers know exactly how many people are on the current page or have followed the conversation, giving them more incentive to leave a thoughtful response.;Comment Notifiers- The widget shows you who is participating in the conversation and what they are saying as it happens, all without losing your place on the page;Media Embedding- Livefyre Comments 3 enhances your media embedding options so that you can share photos from Flickr and Instagram, play videos from YouTube and Vimeo, flip-through slide decks on SlideShare, listen to songs from SoundCloud and Spotify, geek-out on animated gifs from Myspace, and even feature Wikipedia articles directly in the conversation stream.;Comment Editing- Comment Editing allows users to edit their own comments within a specific time frame so that they can fix their spelling mistakes without changing the context of the following comment stream. Site admins can make edits to all comments as well.;User Profiles- Our user bios allow community members to share a bit about themselves and the topics they care about, while also showing the conversations where they have left comments.;Spam Protection- thanks to real-time machine-learning protection your comments are basically spam-free.;Community Flagging- Multiple admin flagging options give you more insight into the quality of the conversation, and community members can leave you a note to provide further explanation.;Leave Comment Notes- Admins can communicate with other moderators on your site to follow-up with a particular commenter, or visit their blog to leave a comment.;Multiple Moderators- Designate as many moderators as you'd like—editors, guest authors, community managers—and kick it up a notch by managing their access levels too.;Ban & Whitelist Users- Streamline moderation by allowing a core group of readers to comment on your site without passing through the moderation process with Whitelists. You can also deal with trolls and flamers quickly and efficiently by designating them as banned users - removing them and their comments from your community.;Profanity Lists- Every comment is processed through Livefyre’s real-time profanity filters, even the words you didn’t even know existed get nixed before they hit the page.;User Activity- Keep track of the most active participants in your community and see what conversations they are taking part in on other Livefyre blogs.;Moderation and Conversation Reports- Get a view of your most active users and conversations at a glance. Organize your reports by date range and easily track which conversations need your attention.
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Open source; NLP; Machine learning
Statistics
GitHub Stars
-
GitHub Stars
192.3K
GitHub Stars
-
GitHub Forks
-
GitHub Forks
74.9K
GitHub Forks
-
Stacks
37
Stacks
3.9K
Stacks
120
Followers
14
Followers
3.5K
Followers
282
Votes
0
Votes
106
Votes
25
Pros & Cons
No community feedback yet
Pros
  • 32
    High Performance
  • 19
    Connect Research and Production
  • 16
    Deep Flexibility
  • 12
    Auto-Differentiation
  • 11
    True Portability
Cons
  • 9
    Hard
  • 6
    Hard to debug
  • 2
    Documentation not very helpful
Pros
  • 9
    Open Source
  • 6
    Docker Image
  • 6
    Self Hosted
  • 3
    Comes with rasa_core
  • 1
    Enterprise Ready
Cons
  • 4
    Wdfsdf
  • 4
    No interface provided
Integrations
No integrations available
JavaScript
JavaScript
Slack
Slack
RocketChat
RocketChat
Google Hangouts Chat
Google Hangouts Chat
Telegram
Telegram
Microsoft Bot Framework
Microsoft Bot Framework
Twilio
Twilio
Mattermost
Mattermost

What are some alternatives to LiveFyre, TensorFlow, rasa NLU?

Disqus

Disqus

Disqus looks to make it very easy and rewarding for people to interact on websites using its system. Commenters can build reputation and carry their contributions from one website to the next.

scikit-learn

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

SpaCy

SpaCy

It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.

Polyaxon

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

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