Definitions:
- TensorFlow: An open-source machine learning framework developed by Google. TensorFlow provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. It supports a wide range of applications, including neural networks, deep learning, and reinforcement learning.
- Test-Driven Development (TDD): A software development approach where tests are written before the actual code. TDD involves writing a test that defines a function or improvement, then producing the minimum amount of code to pass that test, and finally refactoring the new code to acceptable standards. This approach ensures that the code meets the specified requirements and is of high quality.
- Text Analytics: The process of transforming unstructured text data into meaningful data for analysis, to measure customer opinions, product reviews, or feedback. Text analytics involves techniques such as natural language processing (NLP), sentiment analysis, and topic modelling to extract insights from textual data.
- Time Series Analysis: The use of statistical techniques to analyse time-ordered data points to extract meaningful statistics and other characteristics of the data. Time series analysis is used to identify trends, seasonality, and anomalies in data over time. It is applied in fields such as finance, economics, and weather forecasting.
- Time Series Forecasting: The process of using historical data to predict future values. Time series forecasting involves applying statistical models and machine learning algorithms to identify patterns and trends in time-ordered data. It is used in applications such as stock market prediction, demand forecasting, and inventory management.
- Tokenisation: The process of breaking down text into smaller units, such as words or phrases, known as tokens. Tokenisation is a fundamental step in natural language processing (NLP) and is used to prepare text data for analysis, enabling tasks such as text classification, sentiment analysis, and machine translation.
- Transfer Learning: A machine learning technique where a model trained on one task is reused as the starting point for a model on a second task. Transfer learning leverages the knowledge gained from the first task to improve the performance and efficiency of the second task. It is particularly useful in domains with limited data, such as image recognition and natural language processing.
- Trigger: An event or condition that initiates a specific action or process. In cloud computing and automation, triggers are used to automate workflows and ensure that actions are taken in response to specific events, such as data updates, user actions, or scheduled times.
- TypeScript: A statically typed superset of JavaScript that adds optional static typing to the language. TypeScript enhances the development process by catching errors early, providing better tooling support, and enabling more robust and maintainable code. It is widely used in frontend and backend development, particularly for large-scale applications.
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