AI glossary for you

Here’s a brief AI glossary for you:

  1. Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems, involving tasks like problem-solving, learning, reasoning, and decision-making.
  2. Machine Learning (ML): A subset of AI that enables systems to learn and improve from experience without being explicitly programmed. It uses algorithms to identify patterns in data.
  3. Deep Learning: A type of machine learning that employs neural networks with multiple layers to process and analyze complex data, often achieving state-of-the-art results in various tasks like image recognition and natural language processing.
  4. Neural Network: A computational model inspired by the human brain’s structure, consisting of interconnected nodes (neurons) that process and transmit information.
  5. Supervised Learning: A machine learning approach where models are trained on labeled data, meaning input data is paired with the correct output, allowing the model to learn relationships between inputs and outputs.
  6. Unsupervised Learning: In this approach, models are given unlabeled data and tasked with finding patterns or structures within the data without explicit guidance.
  7. Reinforcement Learning: A learning paradigm where an agent interacts with an environment, learning to perform actions to maximize a reward signal. The agent learns through trial and error.
  8. Natural Language Processing (NLP): A field of AI focused on enabling machines to understand, interpret, and generate human language. It encompasses tasks like language translation, sentiment analysis, and chatbot development.
  9. Computer Vision: A branch of AI that enables machines to interpret and understand visual information from the world, including tasks like image recognition, object detection, and facial recognition.
  10. Algorithm: A set of rules or instructions designed to solve a specific problem or complete a particular task.
  11. Data Mining: The process of discovering patterns, correlations, or insights from large datasets, often using techniques from statistics and machine learning.
  12. Feature Extraction: The process of selecting or transforming relevant characteristics or features from raw data to make it suitable for machine learning algorithms.
  13. Model Evaluation: Assessing the performance of a machine learning model using various metrics to determine how well it generalizes to new, unseen data.
  14. Overfitting: When a machine learning model performs exceptionally well on the training data but fails to generalize to new data due to capturing noise and irrelevant patterns.
  15. Bias and Fairness: The presence of systematic errors in AI systems, often stemming from biased training data, that result in unequal or unfair treatment of different groups.
  16. Chatbot: A computer program designed to simulate conversation with human users, often used for customer service, information retrieval, and interactive experiences.
  17. AI Ethics: The study of ethical issues surrounding the design, implementation, and use of AI systems, including concerns about bias, privacy, transparency, and accountability.
See also  গুরুত্বপূর্ণ সাধারণ জ্ঞান বাংলাদেশ ও আন্তর্জাতিক বিষয়াবলী

AI glossary for you

Learning AI

আপনার মঙ্গল কামনায়

এই চাকরির জন্য প্রতিষ্ঠান আপনার কাছ থেকে কোন অর্থ চাইলে অথবা কোন ধরনের ভুল বা বিভ্রান্তিকর তথ্য দিলে আমরা দায়ী নয়। চাকরি পাওয়ার জন্য কোন ব্যাক্তি / প্রতিষ্ঠানকে অর্থ প্রদান করতে আমরা কাউকে উৎসাহিত করিনা। কোন প্রকার অর্থ লেনদেনের দায়িত্ব আমরা বহন করবো না।

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