Traditional Anti-Money Laundering (AML) engines are fundamentally blind to organized financial crime. Legacy rule-based systems and standard machine learning models evaluate transactions in isolated, tabular rows. They ask: “Is this…
Traditional Computer Vision (CV) pipelines in digital banking are fundamentally broken. When engineering teams attempt to build automated Know Your Customer (eKYC) flows, they usually try to train custom Convolutional…
From Jupyter to Production: Deploying a Low-Latency VAE Anomaly Detector with ONNX & FastAPI 📌 What You’ll Learn in This Guide 📌 Objectives Standard classification models crumble when 99% of…
Formalizing Structural Context Aggregation in Graph Neural Networks 1. Non-Euclidean Topological Abstractions Traditional deep learning architectures rely on rigid Euclidean spatial geometries. Convolutional Neural Networks (CNNs) assume flat 2D lattices…
To meet the engineering standards of top-tier research institutes like IIT or IISc, a resume project cannot rely on high-level wrappers like LangChain or stock FAISS integrations. It must demonstrate…
To meet the engineering standards of top-tier research institutes like IIT or IISc, a resume project cannot rely on high-level wrappers like LangChain or stock FAISS integrations. It must demonstrate…
Guardrailed RAG Engine for High-Compliance Data Enterprise Document Intelligence & Guardrailed RAG Systems are currently among the highest-demand architectural patterns in FinTech, Healthcare, and Legal Tech. This project implements a…
Datasets, Graph Representations, and Inference Mechanics Graph Neural Networks (GNNs) extend traditional deep learning models to graph-structured data G = (V, E) consisting of vertices V and edges E. Unlike…
From Graph Math to PyTorch Implementation Unlike images (regular 2D grids) or text (1D sequences), graph structures represent relational data with arbitrary topologies. Graph Neural Networks (GNNs) operate directly on…
Architectural Bottlenecks and the HyGCN Paradigm Graph Neural Networks (GNNs) have emerged as the standard approach for machine learning on non-Euclidean structured data. However, executing GNN workloads on conventional CPU…
