General AI Concepts
- Artificial Intelligence (AI)
- A branch of computer science focused on creating systems capable of performing tasks that would typically require human intelligence.
- Algorithm
- A set of rules or procedures for solving a problem, often implemented in computer code.
- Data Set
- A collection of data used to train or test AI models.
- Inference
- The process of using a trained AI model to make predictions or decisions based on new data.
Machine Learning Concepts
- Machine Learning (ML)
- A subset of AI that allows computers to learn from data and make decisions without explicit programming.
- Supervised Learning
- A type of machine learning where an algorithm is trained on labeled data.
- Unsupervised Learning
- A type of machine learning where an algorithm learns patterns from unlabeled data.
- Reinforcement Learning
- A type of machine learning where an agent learns to make decisions by interacting with an environment to achieve a goal.
- Overfitting
- When an AI model learns the training data too well, including its noise and outliers, and performs poorly on new, unseen data.
- Underfitting
- When an AI model is too simple to capture the underlying patterns in the data, leading to poor performance.
- Feature Extraction
- The process of selecting or transforming variables (features) from the data to improve an AI model's performance.
Deep Learning Concepts
- Deep Learning
- A specialized form of machine learning inspired by the architecture and function of the brain, using neural networks with many layers.
- Neural Network
- A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons).
- Convolutional Neural Network (CNN)
- A type of neural network particularly effective for tasks like image recognition.
- Recurrent Neural Network (RNN)
- A type of neural network designed to handle sequential data like time series or natural language.
- Backpropagation
- The primary algorithm for performing gradient descent on neural networks, used to minimize the error in the model's predictions.
- Activation Function
- A mathematical function applied to a node in a neural network, determining the node's output.
Generative AI Concepts
- Generative AI
- A branch of AI focused on creating new data that resembles a given dataset.
- Generative Adversarial Network (GAN)
- A type of neural network used for generative tasks, consisting of a Generator and a Discriminator that are trained together.
- Autoencoder
- A type of neural network used for unsupervised learning, often for dimensionality reduction or feature learning.
- Transformer Architecture
- A type of neural network architecture particularly effective for tasks involving sequences, like natural language processing.