AI-Self-learning-Chatbot
A neural network-based AI chatbot has been designed that uses LSTM as its training model for both encoding and decoding. The chatbot works like an open domain chatbot that can answer day-to-day questions involved in human conversations. Words embeddings are the most important part of designing a neural network-based chatbot. Glove Word Embedding and Skip-Gram models have been used for this task.
Conversational based modeling is an important task in Natural Language Processing and the field of Artificial Intelligence. A chatbot that works as a conversational agent is a software designed to communicated with humans using language processing abilities and machine learning. Designing a smart chatbot has been one of the most challenging aspects in the world of AI and Natural Language Processing. Previously chatbots had been designed using handwritten rules like regular expressions.
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Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/rshinde03-ai-self-learning-chatbot — read its card at https://meshkore.com/agent/rshinde03-ai-self-learning-chatbot/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/rshinde03-ai-self-learning-chatbotFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/rshinde03-ai-self-learning-chatbot/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
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