| Term | Description |
| Machine Learning (ML) | Machine learning is a type of AI that enables machines to learn and improve from experience without being explicitly programmed. It uses algorithms and statistical models to analyze and identify patterns in data and make predictions based on that data. |
| Deep Learning | Deep learning is a subset of machine learning that uses neural networks with multiple layers to analyze and process large amounts of data. It is widely used for tasks such as image and speech recognition, natural language processing, and decision-making. |
| Neural Networks | Neural networks are a type of computing system that is designed to function like a human brain. They are composed of layers of interconnected nodes that process information and make decisions based on that information. |
| Natural Language Processing (NLP) | NLP is a branch of AI that deals with the interaction between machines and human language. It is used to enable machines to understand and interpret human language, as well as to generate human-like language. |
| Computer Vision | Computer vision is an area of AI that focuses on enabling machines to interpret and analyze visual information from the world around them. It is used for tasks such as image and video recognition, object detection, and facial recognition. |
| Reinforcement Learning | Reinforcement learning is a type of machine learning that uses trial and error to learn from experience. It involves a machine taking actions in an environment and receiving feedback in the form of rewards or penalties based on those actions. |
| Robotics | Robotics is a field that combines AI, computer science, and engineering to create intelligent machines that can perform physical tasks in the real world. It is used in a variety of applications, such as manufacturing, healthcare, and transportation. |
| Supervised Learning | Supervised learning is a type of machine learning where the algorithm is trained on labeled data, where the desired output is already known. The algorithm learns to make predictions based on the input data and the corresponding labeled output. |
| Unsupervised Learning | Unsupervised learning is a type of machine learning where the algorithm is trained on unlabeled data, where the desired output is not known. The algorithm learns to identify patterns and relationships in the data without any prior knowledge of the output. |
| Transfer Learning | Transfer learning is a technique in machine learning where a pre-trained model is used as a starting point for a new task. The pre-trained model is fine-tuned on a new dataset to perform a new task, which can save time and resources compared to training a new model from scratch. |
| Artificial Neural Network (ANN) | An artificial neural network is a type of computing system that is modeled after the structure and function of the human brain. It consists of interconnected nodes or neurons that process and transmit information. |
| Convolutional Neural Network (CNN) | A convolutional neural network is a type of neural network that is particularly suited for processing visual data, such as images and videos. It uses convolutional layers to identify features in the data and pooling layers to reduce the dimensionality of the data. |
| Recurrent Neural Network (RNN) | A recurrent neural network is a type of neural network that is particularly suited for processing sequential data, such as time series data and natural language processing. It uses feedback loops to process information from previous time steps and update its internal state. |
| Artificial Intelligence Ethics | AI ethics is the study of the ethical implications of AI systems and technologies. It involves addressing issues such as bias, fairness, accountability, transparency, privacy, and human values in the development and deployment of AI systems. |
| Generative Adversarial Networks (GANs) | GANs are a type of neural network that consists of two models a generator and a discriminator. The generator generates new data, while the discriminator evaluates the authenticity of the generated data. The two models are trained together in a competitive process until the generator produces data that is indistinguishable from real data |
| Bayesian Networks | A Bayesian network is a probabilistic graphical model that represents the relationships between variables in a system. It is used for probabilistic reasoning, decision-making, and prediction |
| Ensemble Learning | Ensemble learning is a technique in machine learning where multiple models are trained on the same data, and their predictions are combined to make a final prediction. It is used to improve the accuracy and robustness of the model |
| Explainable AI (XAI) | Explainable AI is an approach to AI development that aims to make AI systems more transparent and understandable to humans. It involves designing AI systems that can provide explanations for their decisions and actions, making it easier for humans to understand and trust the system |
| Edge Computing | Edge computing is a computing paradigm that involves processing data on devices located at the edge of the network, rather than in a centralized data center. It is used to reduce latency and improve performance for applications that require real-time processing, such as IoT devices and autonomous vehicles |
| Swarm Intelligence | Swarm intelligence is a type of AI that is inspired by the collective behavior of social insects, such as ants and bees. It involves designing algorithms that mimic the behavior of these organisms, such as foraging, communication, and cooperation, to solve complex problems |
| Hyperparameters | Hyperparameters are the parameters of a machine learning model that are set before training and affect the performance of the model. Examples of hyperparameters include learning rate, batch size, and number of layers in the neural network. |
| Data Augmentation | Data augmentation is a technique used in machine learning to increase the size of the training dataset by creating new variations of the existing data. It is used to improve the generalization and robustness of the model. |
| Autoencoder | An autoencoder is a type of neural network that is used for unsupervised learning. It learns to encode the input data into a lower-dimensional representation and then decode it back to the original data. It is used for tasks such as data compression and denoising. |
| Natural Language Generation (NLG) | Natural language generation is a subfield of NLP that involves generating natural language text from structured data. It is used for tasks such as chatbots, virtual assistants, and report generation. |
| Knowledge Graphs | A knowledge graph is a type of database that represents knowledge in a structured and machine-readable format. It consists of nodes and edges that represent entities and their relationships, respectively. It is used for tasks such as information retrieval and question answering. |
| Object Recognition | Object recognition is a computer vision task that involves identifying and localizing objects in an image or video. It is used for tasks such as autonomous driving and surveillance. |
| Adversarial Attacks | Adversarial attacks are a type of cyberattack that involve manipulating input data to fool an AI system into making incorrect decisions. Adversarial attacks can be used to circumvent security measures, such as facial recognition systems and spam filters. |
| Decision Trees | A decision tree is a type of algorithm that is used for supervised learning. It builds a tree-like model of decisions and their possible consequences based on the input data. It is used for tasks such as classification and regression. |
| Speech Recognition | Speech recognition is an NLP task that involves transcribing spoken language into text. It is used for tasks such as virtual assistants and speech-to-text applications. |