Telemedicine: Involves the use of AI and telecommunications technologies to provide remote medical consultations and healthcare services.
TensorFlow: An open-source machine learning framework developed by Google, widely used for building and training AI models, particularly deep neural networks.
Test Data: A subset of data used to evaluate the performance and accuracy of AI models, typically separated from the training data to assess generalization.
Text Classification: An AI task where text data is categorized into predefined classes or labels, such as spam detection or sentiment analysis.
Text Generation: An AI task where models generate human-like text, often used in applications like chatbots, content creation, and automated writing.
Text Generation: An AI task where models generate human-like text, often used in applications like chatbots, content creation, and automated writing.
Text Mining: Also known as text analytics, is the process of extracting valuable insights and information from unstructured text data, such as documents, emails, and social media content.
Time Complexity: A measure of the computational resources required by an algorithm or AI model to perform a task, often analyzed for efficiency.
Time Series Analysis: A statistical technique used in AI for studying and forecasting data points collected or recorded over successive time intervals, commonly used in finance and forecasting.
Time Series Forecasting: An AI task where models predict future data points based on historical time series data, often used in financial forecasting and demand prediction.
Tokenization: The process of breaking text into individual units, often words or phrases, to facilitate natural language processing and analysis.
Topic Modeling: A natural language processing technique that identifies topics or themes within a collection of text documents, helping with content organization and analysis.
Training Data: The dataset used to train an AI model, containing labeled examples that guide the model’s learning process.
Training Set: A portion of the dataset used to train an AI model, containing labeled examples that guide the model’s learning process.
Transfer Function: In neural networks, a transfer function, also known as an activation function, introduces non-linearity to the model, allowing it to capture complex relationships in data.
Transfer Learning: A machine learning technique where an AI model is trained on one task and then fine-tuned or adapted for another related task, often reducing the need for extensive new training data.
