Graph Neural Networks (GNNs) have emerged as a powerful class of models designed to work with graph-structured data. These models have revolutionized fields such as social network analysis, drug discovery, and recommendation systems, where data relationships are better represented in the form of nodes (entities) and edges (relationships). At MHTECHIN, we specialize in applying GNNs
In the world of deep learning, ensuring model robustness against adversarial attacks is critical for deploying AI systems in real-world applications. Adversarial attacks involve subtly perturbing input data to deceive models into making incorrect predictions, posing significant security and reliability risks. Adversarial training has emerged as a leading defense mechanism to mitigate these threats by
Introduction In deep learning (DL), data is the cornerstone of success. Managing the flow of data—from collection to preprocessing, storage, and feeding it to the model—is crucial for building efficient and scalable AI systems. A well-designed data pipeline ensures that data is processed accurately, efficiently, and in a timely manner. At MHTECHIN, we specialize in