Beyond Basic RAG: A Practical Guide to Advanced Indexing Techniques
Author(s): Saif Ali Kheraj Originally published on Towards AI. https://en.wikipedia.org/wiki/Retrieval-augmented_generation#/media/File:RAG_diagram.svg Retrieval Augmented Generation (RAG) has become the go to approach for building AI systems that can access and reason over large document collections. But here is the reality most developers face: basic …
Applied Mathematics: The Hidden Engine Powering Tomorrow’s Breakthroughs
Author(s): Saif Ali Kheraj Originally published on Towards AI. How applied mathematicians solve real-world optimization problems in industry In the world of operations research and applied mathematics, few algorithms are as elegant and powerful as the simplex method for solving linear programming …
Dense Passage Retrieval (2020) and Contriever (2021): The Models That Paved the Way for Future, Smarter LLMs
Author(s): Saif Ali Kheraj Originally published on Towards AI. Dense Passage Retriever (DPR) marked a turning point in open-domain question answering when it launched in 2020. It demonstrated that dense vector representations, learned through deep neural networks, can outperform traditional sparse retrieval …
A Deep Technical Exploration of Retrieval-Augmented Generation (RAG) with Transformers, DPR, FAISS, and BART
Author(s): Saif Ali Kheraj Originally published on Towards AI. RAG stands for Retrieval-Augmented Generation. It’s a clever setup where a transformer model (you know, the brains behind all gpts) doesn’t just make things up — it actually goes out, finds real information, …
Why QLoRA Changes the Game: A Quick Dive into Efficient Fine-Tuning with BERT
Author(s): Saif Ali Kheraj Originally published on Towards AI. Quantized Low-Rank Adaptation — anyone with a mid-range GPU and some curiosity can now fine-tune powerful models without burning through a budget or a power supply. In this article, we will break down …
Training Less, Achieving More: Unlocking Transformers with LoRA
Author(s): Saif Ali Kheraj Originally published on Towards AI. https://arxiv.org/pdf/2106.09685 In the era of large language models, Transformer is like the original brain of AI. But they come with a catch: Full fine tuning them is like …. Enter LoRA (Low-Rank Adaptation) …
Parameter-Efficient Finetuning (PEFT) and Adapter Modules in Transformers
Author(s): Saif Ali Kheraj Originally published on Towards AI. Fine-tuning large pre-trained models is an essential step in adapting them to specific tasks. However, traditional full fine-tuning requires updating all parameters, leading to high computational costs, increased memory usage, and a risk …
FactoryBERT: An AI That Understands Manufacturing
Author(s): Saif Ali Kheraj Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. https://www.freepik.com/free-photos-vectors/manufacturing-process Factories have their own way of talking. If you’ve ever been inside one, you might hear things like: “OEE is …
Data Diagnostics: Transforming & Reducing Data for Smarter Insights
Author(s): Saif Ali Kheraj Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. Ever looked at a dataset and wondered, Where do I even start? The answer lies in understanding its distribution. Before jumping …
Modern Analytics Framework — Industry Approach
Author(s): Saif Ali Kheraj Originally published on Towards AI. Analytics Methodologies, Frameworks, and Applications This member-only story is on us. Upgrade to access all of Medium. In this post, I am going to talk about analytics from an organization’s perspective, mixing in …
Solving Facility Location
Author(s): Saif Ali Kheraj Originally published on Towards AI. We will start with one important use case to solve a facility location problem. Suppose we have a service-based company named MMM that sells different products to customers across various regions. Every year, …
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