Definitions:
- Human-Computer Interaction (HCI): A field of study focusing on the design of computer technology and the interaction between humans and computers. HCI aims to create user-friendly interfaces and experiences by understanding user needs, behaviours, and cognitive processes. This involves researching and designing interfaces that are intuitive, efficient, and enjoyable to use, ensuring that technology serves the needs of its users effectively. HCI is crucial in the development of user-centred products and services.
- Human-Centred Design: An approach to problem-solving that starts with the people you’re designing for and ends with new solutions that are tailor-made to suit their needs. Human-centred design involves empathising with users, defining problems, ideating solutions, prototyping, and testing. It emphasises the importance of understanding user needs, contexts, and behaviours to create products, services, and experiences that are meaningful and valuable to users. This approach is fundamental in user experience (UX) design and ensures that digital products meet user expectations and improve their lives.
- Hyperparameter Optimisation: The process of finding the optimal set of hyperparameters for a machine learning algorithm. Hyperparameters are parameters whose values are set before the learning process begins. They control the learning process itself, such as the learning rate or the number of hidden layers in a neural network. Optimising hyperparameters is crucial for improving the performance of machine learning models. Techniques such as grid search, random search, and Bayesian optimisation are commonly used to find the best hyperparameters.
- Hyperparameters: Parameters whose values are used to control the learning process. Unlike parameters that are learned from the data, hyperparameters are set before training begins. Examples of hyperparameters include the learning rate, the number of trees in a random forest, and the number of layers in a neural network. Selecting appropriate hyperparameters is essential for the success of machine learning models, as they significantly influence the model's performance and accuracy.
- Hadoop: An open-source framework used for distributed storage and processing of large data sets across clusters of computers. Hadoop is a key component of the big data ecosystem and is widely used for data processing, machine learning, and analytics. It consists of several modules, including the Hadoop Distributed File System (HDFS) for storage and MapReduce for processing large data sets. Hadoop enables scalable and cost-effective data processing, making it a cornerstone of big data technologies.
- HDFS (Hadoop Distributed File System): A distributed file system used by the Hadoop framework to store large amounts of data across a cluster of machines. HDFS provides high throughput access to application data and is designed to handle the storage of large data sets reliably. It achieves fault tolerance by replicating data across multiple nodes in the cluster, ensuring data availability and reliability. HDFS is a fundamental component of the Hadoop ecosystem and is widely used in big data processing and analytics.
- HPC (High-Performance Computing): The use of supercomputers and parallel processing techniques to perform complex computations and process large amounts of data at high speeds. HPC is essential for scientific research, engineering simulations, data analysis, and machine learning. Cloud providers offer HPC services, allowing organisations to access powerful computing resources on demand. HPC enables the processing of massive data sets and the execution of complex algorithms, supporting advancements in various fields.
- Hive: An open-source data warehousing solution built on top of Apache Hadoop for querying and analysing large data sets stored in Hadoop files. Hive provides a SQL-like interface called HiveQL for querying data, making it easier for users familiar with SQL to work with big data. Hive is widely used for data warehousing, data mining, and business intelligence applications. It enables efficient data querying and processing, supporting the analysis of large-scale data sets.
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