My Courses
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A Guide to Running LLMs Locally with No-Code Framework Dify
Running LLMs locally on your CPU with Dify and Ollama opens up a world of possibilities for AI enthusiasts, developers, and privacy-conscious users.
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Optimizing LLM Inference for Faster Results Using Quantization – A Hands on Guide
Optimizing LLM inference through quantization is a powerful strategy that can dramatically enhance performance while slightly reducing accuracy
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Multilingual Tokenization Efficiency in Large Language Models: A Study on Indian Languages
Authors: Mohamed Azharudeen M, Balaji Dhamodharan
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Harnessing LLMs for Time Series Forecasting: Developing a Swap-Based Hedging Strategy for Commodity Trading
Authors: Sriram Gudimella, Rohit Zajaria, Jagmeet Sarna
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Elevating Fairness in Consumer Credit Assessments: A Large Language Model (LLM) Driven Approach
Authors: Shubhradeep Nandi, Kalpita Roy
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Enhancing Large Language Models: Integrating Human Preferences and Conditional Reinforcement Learning
Authors: Suvojit Hore, Gayathri Nadella, Sanmathi Vaman Parvatikar
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Generative AI in Financial Crime Investigation: Enhancing Suspicious Activity Analysis
Authors: Varun Aggarwal, Charchit Bahl, Rahav Manoharan, Pushkar Raj
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Assessing the Effectiveness of Generative Adversarial Networks for Time Series Data Augmentation
Author: Srinivas Babu Ratnam
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How to Evaluate the RAG Pipeline Cost?
This article details the key factors influencing RAG pipeline cost, covering implementation, operation, and data expenses.
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HybridRAG: Merging Structured and Unstructured Data for Cutting-Edge Information Extraction
HybridRAG integrates Knowledge Graphs and Vector Retrieval to enhance accuracy and speed in complex data extraction tasks.
