Definitions:
- Parallel Computing: A type of computation in which many calculations or processes are carried out simultaneously. Parallel computing is used to accelerate the execution of complex tasks by dividing them into smaller parts that can be processed concurrently. This approach is crucial in fields such as high-performance computing, data science, and AI, where large-scale data processing and intensive computations are required.
- Parameter: A variable that is used in a function or algorithm to define its behaviour. Parameters are inputs that affect the output of the function. In machine learning, parameters are the internal variables that the model uses to fit the data, such as the weights and biases in a neural network.
- Partitioning: The process of dividing a database or dataset into smaller, more manageable parts. Partitioning improves performance by allowing parallel processing and efficient data retrieval. In big data and cloud computing, partitioning is used to distribute data across multiple nodes, enabling scalable and efficient data processing.
- Pattern Recognition: A branch of machine learning that focuses on the recognition of patterns and regularities in data. Pattern recognition involves the identification and classification of patterns in data, such as images, signals, and text. It is used in applications such as facial recognition, speech recognition, and text classification.
- Personalisation: The process of tailoring the user experience to the individual needs, preferences, and behaviours of each user. Personalisation involves collecting and analysing user data to provide customised content, recommendations, and interactions. This approach enhances user engagement, satisfaction, and loyalty in digital products and services.
- Platform as a Service (PaaS): A cloud computing service model that provides a platform for developing, running, and managing applications without the complexity of building and maintaining the infrastructure typically associated with developing and launching an app. PaaS enables developers to focus on building software rather than managing infrastructure.
- Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Predictive analytics is used to forecast trends, detect anomalies, and make data-driven decisions in various domains, including marketing, finance, and healthcare.
- Predictive Maintenance: The use of predictive analytics and machine learning to anticipate equipment failures and schedule maintenance before the failures occur. Predictive maintenance helps to optimise the lifecycle of equipment, reduce downtime, and improve operational efficiency in industries such as manufacturing, transportation, and energy.
- Privacy-Preserving Machine Learning: Techniques and methods to ensure that machine learning models protect the privacy of the data they process. Privacy-preserving machine learning involves using techniques such as differential privacy, federated learning, and homomorphic encryption to maintain data confidentiality while enabling accurate and useful model training and inference.
- Probabilistic Programming: A programming paradigm that combines probability theory with programming to create models that can handle uncertainty and make probabilistic predictions. Probabilistic programming languages enable the specification of complex probabilistic models and the efficient inference of their parameters from data.
- Product Management: The process of guiding the success of a product and leading the cross-functional team responsible for improving it. Product management involves defining the product vision, gathering and prioritising user needs, and ensuring that the product meets business objectives and user expectations. It is a crucial role in the development and success of digital products and services.
- Progressive Web Apps (PWAs): Web applications that use modern web capabilities to deliver an app-like experience to users. PWAs are designed to work on any device and enhance the user experience by providing features such as offline functionality, push notifications, and fast loading times. They combine the best of web and mobile apps to create a seamless and engaging user experience.
- Prompt Engineering: The process of designing input (prompts) for AI models to generate desired outputs. Prompt engineering involves crafting specific instructions or queries that guide the model to produce accurate and relevant responses. This technique is particularly important in natural language processing and AI-driven applications, where the quality of the input prompt significantly affects the output.
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