rasa NLU vs TensorFlow vs WalkMe

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rasa NLU

123
282
+ 1
25
TensorFlow

3.8K
3.4K
+ 1
106
WalkMe

25
72
+ 1
0

TensorFlow vs WalkMe vs rasa NLU: What are the differences?

<Write Introduction here>

1. **Language Support**: TensorFlow supports multiple programming languages like Python, C++, and Java, while WalkMe and Rasa NLU are primarily focused on Python for development.
2. **Application Focus**: WalkMe is a customer experience platform used for creating interactive on-screen guidance, while TensorFlow is a machine learning library focusing on dataflow and differentiable programming. Rasa NLU, on the other hand, is a natural language understanding tool specifically designed for conversational AI applications. 
3. **Popularity and Community**: TensorFlow has a larger community and extensive documentation compared to WalkMe and Rasa NLU, which may result in more easily accessible resources and support for developers.
4. **Ease of Use**: WalkMe provides a user-friendly interface for creating on-screen walkthroughs without the need for coding, while TensorFlow and Rasa NLU require some level of programming skills for implementation and customization.
5. **Third-Party Integration**: Rasa NLU integrates well with different chatbot platforms, enabling developers to create chatbot applications with ease. TensorFlow also offers various integration options, but with a more general focus on machine learning applications.
6. **Cost Consideration**: WalkMe is a commercial product, requiring a subscription to access its full range of features, whereas both TensorFlow and Rasa NLU are open-source tools that can be used freely without any licensing costs.

In Summary, The key differences between TensorFlow, WalkMe, and Rasa NLU lie in their language support, application focus, popularity, ease of use, third-party integration, and cost consideration. Each tool offers unique features tailored to specific development needs. 
Decisions about rasa NLU, TensorFlow, and WalkMe

Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.

I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.

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Xi Huang
Developer at University of Toronto · | 8 upvotes · 90.4K views

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.

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Pros of rasa NLU
Pros of TensorFlow
Pros of WalkMe
  • 9
    Open Source
  • 6
    Docker Image
  • 6
    Self Hosted
  • 3
    Comes with rasa_core
  • 1
    Enterprise Ready
  • 32
    High Performance
  • 19
    Connect Research and Production
  • 16
    Deep Flexibility
  • 12
    Auto-Differentiation
  • 11
    True Portability
  • 6
    Easy to use
  • 5
    High level abstraction
  • 5
    Powerful
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    Cons of rasa NLU
    Cons of TensorFlow
    Cons of WalkMe
    • 4
      No interface provided
    • 4
      Wdfsdf
    • 9
      Hard
    • 6
      Hard to debug
    • 2
      Documentation not very helpful
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      - No public GitHub repository available -
      - No public GitHub repository available -

      What is rasa NLU?

      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.

      What is TensorFlow?

      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.

      What is WalkMe?

      WalkMe enables website owners and app developers to easily create multiple interactive on-screen Walk-Thru’s that help users to quickly and easily complete even the most complex tasks.

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      What companies use rasa NLU?
      What companies use TensorFlow?
      What companies use WalkMe?

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      What tools integrate with rasa NLU?
      What tools integrate with TensorFlow?
      What tools integrate with WalkMe?
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        What are some alternatives to rasa NLU, TensorFlow, and WalkMe?
        Dialogflow
        Give users new ways to interact with your product by building engaging voice and text-based conversational apps.
        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.
        NLTK
        It is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.
        Transformers
        It provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
        Gensim
        It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
        See all alternatives