xatkit VS Keras

While we wait for these tools to pop up, any tech question on the internals of Xatkit you'd like to know? And if you want to read more about the technologies we have listed above, this twitter thread gives some pointers to good tutorials for them:

(obviously) Create your own chatbots (pairing it up with Xatkit or any other chatbot platform for all the front-end and behaviour processing components)

The easiest part is to create the chatbot. We'll obviously use Xatkit for this. The bot can have as many intents as you wish. The only part that we care about here is the default fallback state. Here, instead of saying something useless, e.g. "sorry I didn't get your question, can you rephrase it and try again?", we will ask Haystack to find us a solution.

Take a look at Xatkit (https://github.com/xatkit-bot-platform/xatkit). It's an open source chatbot development platform and very easy to deploy on your own premises as the bot is compiled into a single .jar.

But, IMHO, many business owners do not really want to create a bot by themselves, no matter how easy is the chatbot development interface. They want to give you the data (whatever type of data they already have, e.g. an excel file with collected questions) and get a bot out of it. This is way at Xatkit we're now providing this type of "chatbot automatic generation services"

There are ML libraries available for every language. So there is always a way to execute/train your neural networks outside the python world. For instance, in Xatkit, we reuse Stanfords' Core NLP models in some of our language processors. And, if needed, there is always the option to wrap the ML models code in a Python server (I like the simplicity of Flask for this) and consume them via API calls to this server.

We have recently launched Xatkit, a pretrained chatbot for eCommerce. The reception so far has been lukewarm and we wonder whether:

Interested to give it a try? For FREE during the next two months? Visit: https://xatkit.com/ (and pls redistribute to your colleagues if you know anybody that could be interested, thanks!)

But this doesn't mean your no-code tool needs to stick to one specific category. As we do in Xatkit, you can offer different interfaces/importers on top of the same engine. You can even offer a low-code version for advanced users willing to use your tool's API to complement the result of the no-code approach.

Keras

As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.

After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow.

All breaking changes are listed here: https://github.com/keras-team/keras/issues/18467

You can use this migration guide to identify and fix each of these issues (and further, making your code run on JAX or PyTorch): https://keras.io/guides/migrating_to_keras_3/

I'm guessing the "real" keras code is coming from the keras repository. Is that a correct assumption? How does that version of Keras get there? If I wanted to write my own activation layer next to ELU, where exactly would I do that?

Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks.

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I'm not aware of anything off-the-shelf, but if you have sufficient programming experience, one way to do this would be to build a large dataset of reference images and pictures and use something like keras to train a convolutional neural network on them.