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Artifical robot

How Does an AI Agent Work? The Basics of Artificial Intelligence in Practice

Introduction to AI Agents Artificial intelligence (AI) is a field of computer science that enables machines to perform tasks requiring intelligence, such as understanding language, recognizing images, or making decisions. One of the most important concepts in AI is the agent. An AI agent is a program or system that can independently observe its environment, […]

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AI in Python

AI Agents in Python – A Simple Project for Beginners

Introduction: Taking Your First Steps with AI Agents in Python Artificial intelligence is increasingly present in everyday life—from intelligent voice assistants and recommendation systems to autonomous vehicles. One of the key elements of AI are agents, which are programs capable of making decisions independently based on data from their environment. Learning how to create your

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AI in real world

Artificial Intelligence in Practice: How to Deploy AI Models in Real-World Projects

Introduction: The Role of AI in Modern Projects Artificial intelligence (AI) has become one of the key elements of digital transformation across many industries. Companies are increasingly using AI to automate processes, analyze large datasets, personalize services, and optimize costs. In practice, implementing AI is not just about building a model, but also about integrating

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Transfer Learning

Transfer Learning in AI: How to Use Pretrained Models for Your Own Applications

Introduction to Transfer Learning What is Transfer Learning and Why is it Important? Transfer learning is an artificial intelligence technique that involves leveraging knowledge gained by a model while solving one task to accelerate and improve learning in another, often related, task. Instead of training a model from scratch, we use pre-trained models that have

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AI Model

Reinforcement Learning in Industrial Applications: Case Studies and Implementations

Introduction What is Reinforcement Learning? Reinforcement Learning (RL) is a branch of machine learning where an agent learns to make decisions by interacting with an environment. Unlike supervised learning, which relies on labeled data, RL is based on a system of rewards and penalties. The agent takes actions in the environment, receives feedback in the

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