xatkit
examples
xatkit | examples | |
---|---|---|
16 | 143 | |
174 | 7,754 | |
1.1% | 0.7% | |
3.1 | 5.3 | |
about 1 month ago | about 1 month ago | |
Jupyter Notebook | ||
Eclipse Public License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
xatkit
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The full tech stack to run a chatbot — behind the scenes of an open source bot platform
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:
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How to build your own chatbot NLP engine
(obviously) Create your own chatbots (pairing it up with Xatkit or any other chatbot platform for all the front-end and behaviour processing components)
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How to program a chatbot that reads all your website and answers questions based on its content
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.
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On premises chatbot
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.
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Chatbots for freelancers or small business
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"
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Choosing Java as your language for a Machine Learning project - Are we crazy???
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.
- Show HN: Chatbots generated from your eCommerce data
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Feedback on our new product and website "Chatbots for e-commerce"
We have recently launched Xatkit, a pretrained chatbot for eCommerce. The reception so far has been lukewarm and we wonder whether:
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(Beta testers needed) Xatkit - pretrained expert eCommerce bots to sell more doing less
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!)
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Beyond no-code: no-learn and no-work development
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.
examples
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My Favorite DevTools to Build AI/ML Applications!
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
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Open Source Ascendant: The Transformation of Software Development in 2024
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.
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Best AI Tools for Students Learning Development and Engineering
Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.
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Releasing The Force Of Machine Learning: A Novice’s Guide 😃
TensorFlow: An open-source machine learning framework for high-performance numerical computations, especially well-suited for deep learning.
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MLOps in practice: building and deploying a machine learning app
The tool used to build the model per se was TensorFlow, a very powerful and end-to-end open source platform for machine learning with a rich ecosystem of tools. And in order to to create the needed script using TensorFlow Jupyter Notebook was used, which is a web-based interactive computing platform.
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🔥14 Excellent Open-source Projects for Developers😎
10. TensorFlow - Make Machine Learning Work for You 🤖
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GPU Survival Toolkit for the AI age: The bare minimum every developer must know
AI models, particularly those built on deep learning frameworks like TensorFlow, exhibit a high degree of parallelism. Neural network training involves numerous matrix operations, and GPUs, with their expansive core count, excel in parallelizing these operations. TensorFlow, along with other popular deep learning frameworks, optimizes to leverage GPU power for accelerating model training and inference.
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
#2 TensorFlow
- Are there people out there who still like Sam atlman - AI IS AT DANGER
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Tensorflow help
I am on a new ftc team trying to get vision to work. I used the ftc machine learning tool chain but I have yet to get a good result with at best a 10% accuracy rate. I have changed everything possible in the tool chain with little luck. To fix this, I have tried making my own .tflite model using the google colab from https://www.tensorflow.org/. When ever I try to run the same code with my own .tflite model, it gives me the error "User code threw an uncaught exception: IllegalStateException - Error getting native address of native library: task_vision_jni". It gives me the same error with official tensor flow tflite test models, and when I put them on a raspberry pi, both worked just fine. Does anyone have a fix to this error or even just tips for the machine learning toolchain?
What are some alternatives?
rasa - 💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
cppflow - Run TensorFlow models in C++ without installation and without Bazel
GerVADER - GerVADER - A German adaptation of the VADER sentiment analysis tool for social media texts
mlpack - mlpack: a fast, header-only C++ machine learning library
WooCommerce - A customizable, open-source ecommerce platform built on WordPress. Build any commerce solution you can imagine.
awesome-teachable-machine - Useful resources for creating projects with Teachable Machine models + curated list of already built Awesome Apps!
Foundation - The most advanced responsive front-end framework in the world. Quickly create prototypes and production code for sites that work on any kind of device.
face-api.js - JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js
sagan - The spring.io site and reference application
Selenium WebDriver - A browser automation framework and ecosystem.
BombPartyBot - A bot for JKLM bomb party
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing