Terms, acronyms, and their meanings related to artificial intelligence that you should know.
• AI - Artificial Intelligence
Artificial Intelligence: A scientific field that aims to create systems capable of performing tasks that require human intelligence, such as learning, reasoning, and pattern recognition.
• ML - Machine Learning
Machine Learning: A branch of artificial intelligence in which systems learn how to automatically improve their performance through experience.
• DL - Deep Learning
Deep Learning: A type of machine learning that uses complex neural networks to analyze large and diverse datasets.
• ANN - Artificial Neural Networks
Artificial Neural Networks: Computational models inspired by biological neural networks, used in information processing.
• NLP - Natural Language Processing
Natural Language Processing: A branch of artificial intelligence concerned with the interaction of computers with human language.
• CV - Computer Vision
Computer Vision: A branch of artificial intelligence focused on enabling machines to understand visual content.
• RNN - Recurrent Neural Networks
Recurrent Neural Networks: A type of neural network characterized by recurring connections that enable it to process sequences of data.
• GAN - Generative Adversarial Networks
Generative Adversarial Networks: A type of neural network used to generate new data similar to the data it was trained on.
• RL - Reinforcement Learning
Reinforcement Learning: A type of machine learning in which a system learns how to behave in an environment through success and failure experiences.
• SVM - Support Vector Machines
Support vector machines: A learning model used to analyze data and identify classification and regression patterns.
• DNN - Deep Neural Networks
Deep neural networks: A type of neural network that contains multiple layers of nodes to complicate the learning and prediction process.
• LSTM - Long Short-Term Memory
Long Short-Term Memory: A special type of recurrent neural network designed to avoid the problem of vanishing gradients and improve the ability to learn long-term associations.
• CNN - Convolutional Neural Networks
Connectional neural networks: A special type of neural network used particularly in image processing and computer vision.
•BERT - Bidirectional Encoder Representations from Transformers
Bidirectional Encoder Representations from Transformers: A natural language processing model based on transformer technology to understand the full context of a word based on all the words in a sentence.
•TTS - Text-to-Speech
Text-to-Speech: A technology that converts written text into spoken language.
•ASR - Automatic Speech Recognition
Automatic speech recognition: A technology that enables computers to interpret and process human speech.
•OCR - Optical Character Recognition
Optical character recognition: A technology that converts printed or typed images into computer-generated text.
•HMM - Hidden Markov Model
Hidden Markov Model: A statistical model used in machine learning for time-sequenced data, such as speech.
•NLU - Natural Language Understanding
Natural language understanding: A branch of natural language processing that focuses on enabling systems to understand the intent of human text.
• IoT - Internet of Things
The Internet of Things: A network of connected physical devices that collect and exchange data, often using artificial intelligence techniques to analyze and interact with that data.
• GPT - Generative Pre-trained Transformer
A natural language processing model used to automatically generate text based on pre-training on large amounts of text data.
• FAIR - Facebook AI Research
Facebook AI Research: A research group at Facebook focused on developing and improving artificial intelligence techniques.
• Seq2Seq - Sequence to Sequence
Sequence to Sequence: A model used in natural language processing to transform an input sequence into an output sequence, such as machine translation.
• ELMo - Embeddings from Language Models
Embedding from Language Models: A natural language processing model used to create dynamic linguistic representations based on the context of words in sentences.
• MT - Machine Translation
Machine translation: The use of software to translate text or speech from one language to another.
•AutoML - Automated Machine Learning
Automated machine learning: The process that allows machine learning models to be developed automatically rather than relying on manual processes.
•Transformer - Transformer Models
Transformer models: A type of AI model widely used in natural language processing that relies on attention mechanisms to improve learning.
Happy learning!
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