Adversarial machine learning studies how machine learning systems can be deliberately manipulated through crafted inputs, poisoned data, or model exploitation—and how such systems can be hardened against these threats. This whitepaper provides a technical introduction to adversarial attacks and defenses,…

Explainable AI (XAI) is the field concerned with making machine learning models more understandable to humans. As predictive models become more complex, especially in high-stakes domains, there is growing need to explain why a model made a prediction, which features…

Federated Learning (FL) is a distributed machine learning paradigm in which models are trained collaboratively across many clients or devices without centralizing raw training data. Instead of sending data to the model, federated learning sends the model to the data,…

Graph Neural Networks (GNNs) are a family of neural architectures designed to operate on graph-structured data. Unlike standard machine learning models that assume independent samples or regular Euclidean grids, GNNs explicitly model entities and their relationships using nodes, edges, neighborhoods,…

Object detection and segmentation are core computer vision tasks that move beyond simple image classification by asking not only what is in an image, but where it is and, in segmentation, which pixels belong to each object or class. This…

The Transformer architecture fundamentally changed natural language processing by replacing recurrence with attention-based sequence modeling. BERT, built on the Transformer encoder, became one of the most influential pretrained language models by introducing bidirectional contextual pretraining at scale. This whitepaper explains…

Natural Language Processing (NLP) is the field of artificial intelligence and computational linguistics concerned with enabling machines to process, represent, understand, generate, and interact through human language. It spans rule-based systems, statistical language models, machine learning pipelines, deep neural architectures,…

Deep Reinforcement Learning (Deep RL) combines reinforcement learning with deep neural networks to solve sequential decision-making problems in high-dimensional state spaces. It enables agents to learn directly from complex observations such as images, sensor streams, and structured feature vectors. This…

Reinforcement Learning (RL) is a machine learning paradigm in which an agent learns through interaction with an environment. Instead of being told the correct output for each input, the agent receives scalar reward signals and must discover which actions lead…