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TensorFlow vs WalkMe vs rasa NLU: What are the differences?
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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.
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.
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.
Pros of rasa NLU
- Open Source9
- Docker Image6
- Self Hosted6
- Comes with rasa_core3
- Enterprise Ready1
Pros of TensorFlow
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
Pros of WalkMe
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Cons of rasa NLU
- No interface provided4
- Wdfsdf4
Cons of TensorFlow
- Hard9
- Hard to debug6
- Documentation not very helpful2