Welcome to the AI Encyclopedia No jargon, No complex explanations....
EyeingAI@EyeingAI
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Apr 02, 2024
~3 min read
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Welcome to the AI Encyclopedia
No jargon, No complex explanations.
Just 43 AI terms (in the simplest way possible):
No jargon, No complex explanations.
Just 43 AI terms (in the simplest way possible):
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➾ AI is not just about tools and prompts. It’s important to have a basic understanding of it’s key concepts.
Today, let's talk about 43 fundamental AI terms, explained in the most straightforward & easy-to-understand manner.
Here are the terms in the SIMPLEST way possible:
Today, let's talk about 43 fundamental AI terms, explained in the most straightforward & easy-to-understand manner.
Here are the terms in the SIMPLEST way possible:
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1. Artificial Intelligence (AI): Technology that makes machines smart and capable of performing tasks like humans.
2. Machine Learning (ML): The ability of machines to learn and improve from experience without being explicitly programmed.
2. Machine Learning (ML): The ability of machines to learn and improve from experience without being explicitly programmed.
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3. Deep Learning: A type of machine learning that uses artificial neural networks to imitate how the human brain works.
4. Natural Language Processing (NLP): The ability of computers to understand and interpret human language.
4. Natural Language Processing (NLP): The ability of computers to understand and interpret human language.
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5. Computer Vision: The ability of computers to understand and interpret visual information from images or videos.
6. Robotics: The field of building and programming robots to perform tasks automatically.
6. Robotics: The field of building and programming robots to perform tasks automatically.
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7. Neural Networks: Computer systems inspired by the human brain that learn patterns & make decisions.
8. Big Data: Large amounts of data that can be analyzed by computers to find patterns & insights.
9. Data Science: The study of data to extract meaningful information.
8. Big Data: Large amounts of data that can be analyzed by computers to find patterns & insights.
9. Data Science: The study of data to extract meaningful information.
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10. Data Mining: The process of discovering patterns & relationships in large datasets.
11. Predictive Analytics: Using historical data to make predictions about future events/outcomes.
12. Supervised Learning: A type of machine learning where models learn from labeled examples.
11. Predictive Analytics: Using historical data to make predictions about future events/outcomes.
12. Supervised Learning: A type of machine learning where models learn from labeled examples.
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13. Unsupervised Learning: A type of machine learning where models learn from unlabeled data to find patterns.
14. Reinforcement Learning: Teaching machines to make decisions through trial and error and rewards or punishments.
15. Transfer Learning: Using knowledge from one task to help solve a different but related task.
14. Reinforcement Learning: Teaching machines to make decisions through trial and error and rewards or punishments.
15. Transfer Learning: Using knowledge from one task to help solve a different but related task.
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16. Artificial Neural Networks: Algorithms that mimic the way human brains process info.
17. Convolutional Neural Networks: Neural networks designed to analyze visual data.
18. Recurrent Neural Networks (RNNs): Neural networks that can handle sequential data like language & speech.
17. Convolutional Neural Networks: Neural networks designed to analyze visual data.
18. Recurrent Neural Networks (RNNs): Neural networks that can handle sequential data like language & speech.
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19. Generative Adversarial Networks (GANs): 2 neural networks, one generating data and the other evaluating its authenticity.
20. Support Vector Machines (SVMs): Algorithms used for classification & regression tasks.
20. Support Vector Machines (SVMs): Algorithms used for classification & regression tasks.
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21. Decision Trees: Models that make decisions by following a series of tree-like structures.
22. Random Forests: Ensembles of decision trees that work together to improve accuracy.
22. Random Forests: Ensembles of decision trees that work together to improve accuracy.
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23. K-Nearest Neighbors(KNN): A simple algorithm that classifies data based on similarity to its neighbors
24. Clustering: Grouping data points based on their similarities.
25. Dimensionality Reduction: Simplifying data by reducing number of features while retaining important info.
24. Clustering: Grouping data points based on their similarities.
25. Dimensionality Reduction: Simplifying data by reducing number of features while retaining important info.
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26. Regression: Predicting a numerical value based on input data.
27. Classification: Assigning categories or labels to data.
28. Overfitting: When a model becomes too specialized in the training data and fails to generalize to new data.
27. Classification: Assigning categories or labels to data.
28. Overfitting: When a model becomes too specialized in the training data and fails to generalize to new data.
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29. Underfitting: When a model is too simple & fails to capture important patterns in the data.
30. Hyperparameters: Settings that control how an ML algorithm learns & makes predictions.
31. Optimization: The process of finding best settings for a machine learning algorithm.
30. Hyperparameters: Settings that control how an ML algorithm learns & makes predictions.
31. Optimization: The process of finding best settings for a machine learning algorithm.
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32. Gradient Descent: An optimization algorithm used to update the model’s parameters based on the error.
33. Backpropagation: A method for calculating how errors propagate through a neural network.
34. Loss Function: A measure of how well a model is performing.
33. Backpropagation: A method for calculating how errors propagate through a neural network.
34. Loss Function: A measure of how well a model is performing.
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35. Activation Function: A mathematical function that introduces nonlinearity to neural networks.
36. Batch Normalization: A technique that normalizes input data to speed up training.
37. Dropout: Randomly deactivating some neurons during training to prevent overfitting.
36. Batch Normalization: A technique that normalizes input data to speed up training.
37. Dropout: Randomly deactivating some neurons during training to prevent overfitting.
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38. Learning Rate: A parameter that controls step size during optimization.
39. Momentum: A technique that helps optimization algorithms converge faster.
40. Early Stopping: Stopping training process early to prevent overfitting when model’s performance on validation data declines.
39. Momentum: A technique that helps optimization algorithms converge faster.
40. Early Stopping: Stopping training process early to prevent overfitting when model’s performance on validation data declines.
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41. Ensemble Learning: Combining predictions of multiple models to improve accuracy.
42. Bias: Systematic errors in a model that cause it to deviate from true values.
43. Variance: Variability of a model’s predictions due to sensitivity to fluctuations in training data.
42. Bias: Systematic errors in a model that cause it to deviate from true values.
43. Variance: Variability of a model’s predictions due to sensitivity to fluctuations in training data.
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And there you have it!
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