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  1. Stackups
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  4. Machine Learning Tools
  5. TensorFlow vs Wit.ai

TensorFlow vs Wit.ai

OverviewDecisionsComparisonAlternatives

Overview

TensorFlow
TensorFlow
Stacks3.9K
Followers3.5K
Votes106
GitHub Stars192.3K
Forks74.9K
Wit.ai
Wit.ai
Stacks33
Followers97
Votes7

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Advice on TensorFlow, Wit.ai

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

Detailed Comparison

TensorFlow
TensorFlow
Wit.ai
Wit.ai

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.

Iti is an API that makes it very easy for developers to create applications or devices that you can talk to. Any app, or any device, like a smart watch, Google Glass, Nest, even a car, can stream audio to the Wit API, and get actionable data in return. We turn speech into actions. Think Twilio for Natural Language, with Stripe-level developer friendliness.

-
Wit.ai makes it easy for developers to build applications and devices that you can talk or text to;Wit.ai learns human language from every interaction;Leverages the community: what's learned is shared across developers;Bots;Mobile Apps;Home automation;Wearable devices;Robots
Statistics
GitHub Stars
192.3K
GitHub Stars
-
GitHub Forks
74.9K
GitHub Forks
-
Stacks
3.9K
Stacks
33
Followers
3.5K
Followers
97
Votes
106
Votes
7
Pros & Cons
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
  • 4
    The interface to train is really easy
  • 3
    It´s free
Integrations
JavaScript
JavaScript
Echo
Echo
Messenger Platform
Messenger Platform
Facebook Workplace
Facebook Workplace

What are some alternatives to TensorFlow, Wit.ai?

Engati

Engati

It is a free chatbot platform to build bots quickly without any coding required. It allows you to build, manage, integrate, train, analyse and publish your personalized bot in a matter of minutes.

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.

Dialogflow

Dialogflow

Give users new ways to interact with your product by building engaging voice and text-based conversational apps.

Telegram Bot API

Telegram Bot API

Bots are third-party applications that run inside Telegram. Users can interact with bots by sending them messages, commands and inline requests. You control your bots using HTTPS requests to our bot API.

Botpress

Botpress

Botpress is an open-source bot creation tool written in TypeScript. It is powered by a rich set of open-source modules built by the community. We like to say that Botpress is like the WordPress of bots; anyone can create and reuse other peo

Keras

Keras

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

Microsoft Bot Framework

Microsoft Bot Framework

The Microsoft Bot Framework provides just what you need to build and connect intelligent bots that interact naturally wherever your users are talking, from text/sms to Skype, Slack, Office 365 mail and other popular services.

Amazon Lex

Amazon Lex

Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions.

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.

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