Glossary for Advanced Users
Deep Learning, a subfield of AI, leverages neural networks with numerous interconnected layers to process vast amounts of data, enabling machines to learn and make complex decisions. Deep Learning excels in tasks like speech recognition, natural language processing, and image classification.
Deep Reinforcement Learning is a subset of Machine Learning that combines Deep Learning and Reinforcement Learning. It involves training AI models to make decisions and take actions based on feedback from their environment. Deep Reinforcement Learning has shown promise in applications such as autonomous vehicles, robotics, and game-playing.
Digital assistants, also known as Virtual Assistants or Chatbots, are AI-powered software applications that can engage in conversations and perform tasks on behalf of users. They leverage Natural Language Processing and Machine Learning to understand user queries, provide information, and execute specific actions. Digital Assistants enhance customer experiences and streamline operations by providing personalised and … Read more
Dimensionality Reduction is the process of reducing the number of variables or features in a dataset while retaining its essential information. By eliminating irrelevant or redundant features, businesses can simplify data analysis, improve model performance, and reduce computational complexity. Dimensionality Reduction techniques include Principal Component Analysis (PCA) and t-SNE.
istributed Computing refers to the use of multiple computers or servers to perform computational tasks in a networked environment. It enables businesses to process large amounts of data and execute complex calculations by distributing the workload across different machines. Distributed Computing facilitates parallel processing, improves scalability, and enhances the speed and efficiency of data processing.
Edge Computing brings computing resources closer to the source of data generation, reducing latency and improving response times. By processing and analysing data locally on edge devices such as IoT Devices or Edge Servers, businesses can handle real-time applications and achieve faster data insights. Edge Computing is especially valuable for AI applications that require low-latency … Read more
Ensemble Learning involves combining multiple Machine Learning models to achieve superior performance and accuracy. By leveraging the “wisdom of the crowd,” Ensemble Learning mitigates the drawbacks of individual models and delivers more robust and reliable predictions.
Ethics in AI focuses on the responsible and ethical use of AI technologies. Businesses must consider the fairness, transparency, accountability, and privacy implications of AI applications to ensure that AI benefits society without causing harm.
Evolutionary Computation is a branch of AI inspired by biological evolution and natural selection. It involves using algorithms to mimic evolutionary processes such as mutation, selection, and reproduction to solve complex optimisation and search problems. Evolutionary Computation finds applications in areas such as algorithm design, data mining, and genetic programming.
