Technology

Introduction: What Are Adversarial Attacks and Data Manipulation in AI? Artificial intelligence models, especially those based on deep learning, have achieved remarkable success in fields such as image recognition, natural language processing, and autonomous systems. However, as these models become more widely adopted, their vulnerabilities are also becoming more apparent. Two of the most significant

Introduction to Generative Models Generative models are a cornerstone of modern artificial intelligence, enabling machines to create new data that resembles existing datasets. Unlike traditional discriminative models, which focus on classifying or predicting outcomes based on input data, generative models learn the underlying distribution of the data itself. This allows them to generate new, synthetic

Introduction to Neuro-Symbolic AI Neuro-symbolic AI is an emerging field that seeks to combine the strengths of neural networks with the capabilities of symbolic reasoning. This hybrid approach aims to bridge the gap between data-driven learning and structured, logic-based knowledge representation, offering new possibilities for building intelligent systems that are both powerful and interpretable. The

Introduction to AI Microservices The rapid evolution of artificial intelligence has driven organizations to seek scalable, flexible, and maintainable ways to deploy machine learning (ML) solutions. One of the most effective architectural paradigms to address these needs is the microservices approach. In this section, we explore what microservices are, why they are particularly beneficial for

Introduction to Custom Deep Learning Architectures The rapid evolution of deep learning has led to the development of a wide range of neural network architectures, many of which are available as ready-to-use models in popular frameworks. However, there are numerous scenarios where off-the-shelf solutions are insufficient, and designing a custom architecture becomes necessary. This introduction

Introduction to Model Optimization and Quantization Optimization and quantization of artificial intelligence models are key topics that enable increased computational efficiency and reduced hardware resource requirements. In the era of growing AI popularity, both in industrial and consumer applications, the ability to run advanced models on devices with limited computational power—such as smartphones, IoT devices,

Introduction to Federated Learning In the era of big data and artificial intelligence, the demand for privacy-preserving machine learning solutions is stronger than ever. Traditional AI models are typically trained on centralized datasets, which often requires collecting sensitive user data in one location. This approach raises significant privacy concerns, especially in sectors like healthcare, finance,

 Introduction In recent years, Convolutional Neural Networks (CNNs) and Transformers have revolutionized the field of artificial intelligence, powering breakthroughs in computer vision, natural language processing, and beyond. However, as AI applications grow more complex and diverse, these traditional architectures sometimes fall short in addressing new challenges. This article explores why CNNs and Transformers may no

Introduction: What are AI Agents and Why Should You Be Interested? Artificial Intelligence (AI) is a field that is dynamically changing the world around us. One of its most important elements is AI agents – programs or systems that can independently make decisions, learn from data, and react to changing conditions. AI agents are present