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Ranjan Kumar
A Seasoned Software Practitioner who loves to build cutting edge AI applications.
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Category: Unstructured Data

AI & ML/Deep Learning/GenAI/Natural Language Processing - NLP/Unstructured Data

🚀Hands-on Tutorial: Fine-tune a Cross-Encoder for Semantic Similarity

Posted on October 8, 2025 by Ranjan Kumar / 0 Comment

🔥 Why Fine-Tune a Cross-Encoder? 1. More Accurate Semantic Judgments: 2. Adapting to Domain-Specific Data Without fine-tuning, the model might miss these domain...

GenAI/Information Retrieval/LLMs/Unstructured Data

🔎 A Deep Dive into Cross-Encoders and How They Work

Posted on October 2, 2025 by Ranjan Kumar / 0 Comment

1️⃣ Introduction In AI systems that retrieve or generate information, ranking quality and relevance are critical. Whether you are building a RAG-based assistant...

AI & ML/GenAI/Information Retrieval/Natural Language Processing - NLP/Unstructured Data

🔎Building a Full-Stack Hybrid Search System (BM25 + Vectors + Cross-Encoders) with Docker

Posted on October 2, 2025 by Ranjan Kumar / 0 Comment

1️⃣ Introduction Search is at the heart of every AI application. Whether you’re building a legal research assistant, a compliance monitoring tool, or an LLM-pow...

GenAI/Information Retrieval/Natural Language Processing - NLP/Unstructured Data

🔎BM25-Based Searching: A Developer’s Comprehensive Guide

Posted on October 2, 2025 by Ranjan Kumar / 1 Comment

📌 Introduction: Why BM25 Matters Imagine you type “best Python tutorials” into a search engine. Millions of web pages match your query—but how does the engine k...

AI & ML/GenAI/LLMs/Natural Language Processing - NLP/Unstructured Data

Question Answer Chatbot using RAG, Llama and Qdrant

Posted on May 19, 2025 by Ranjan Kumar / 0 Comment

1. Introduction I have created this teaching chatbot that can answer questions from class IX, subject SST, on the topic “Democratic politics“. I hav...

AI & ML/GenAI/Natural Language Processing - NLP/Unstructured Data

Hands-on Tutorial on Making an Audio Bot using LLM, and RAG

Posted on January 27, 2025 by Ranjan Kumar / 1 Comment

1. Learning Outcome 2. Large Language Model Large Language Model (LLM) is an artificial intelligence (AI) model with billions of parameters, trained on huge amo...

Unstructured Data

How to Record, Save and Play audio in Python?

Posted on December 5, 2024 by Ranjan Kumar / 2 Comments

Libraries The following are the required libraries. Check mic check Check if you have a working microphone on your system. Following is the code snippet you can...

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  • 🚀Hands-on Tutorial: Fine-tune a Cross-Encoder for Semantic Similarity
  • 🔎 A Deep Dive into Cross-Encoders and How They Work
  • 🔎Building a Full-Stack Hybrid Search System (BM25 + Vectors + Cross-Encoders) with Docker
  • 🔎BM25-Based Searching: A Developer’s Comprehensive Guide
  • ✨When Models Stand Between Us and the Web: The Future of the Internet in the Age of Generative AI✨
  • 🚀 Cursor AI Code Editor: Boost Developer Productivity with MCP Servers
  • Building Privacy-Preserving Machine Learning Applications in Python with Homomorphic Encryption
  • Provenance in AI: Auto-Capturing Provenance with MLflow and W3C PROV-O in PyTorch Pipelines – Part 4
  • Navigating AI Risks with NIST’s AI Risk Management Framework (AI RMF)
  • Provenance in AI: Building a Provenance Graph with Neo4j – Part 3
  • Provenance in AI: Tracking AI Lineage with Signed Provenance Logs in Python – Part 2
  • Provenance in AI: Why It Matters for AI Engineers – Part 1
  • LLMs for SMEs – 001: How Small Businesses Can Leverage AI Without Cloud Costs
  • LLM-Powered Chatbots: A Practical Guide to User Input Classification and Intent Handling
  • Reranking for RAG: Boosting Answer Quality in Retrieval-Augmented Generation
  • ChatML: The Structured Language Behind Conversational AI
  • Fast Face Search (Billion-scale Face Recognition) using Vector DB (Faiss)
  • Question Answer Chatbot using RAG, Llama and Qdrant
  • On Emergent Abilities of Large Language Models
  • Prompt Engineering Deep Dive: Parameters, Chains, Reasoning, and Guardrails

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  • Deep Reinforcement Learning
  • Explainable AI
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  • Information Retrieval
  • IoT / Edge Computing
  • LLMs
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  • Natural Language Processing – NLP
  • Security
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  • Unstructured Data
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