Atharv Gyan

Introduction to Edge Computing and IoT Edge computing refers to the practice of processing data near the edge of the network, where the data is generated, rather than relying solely on centralized data-processing warehouses or cloud-based systems. This approach contrasts with traditional cloud computing, where data is transmitted to centralized data centers for processing and analysis. The Internet of Things (IoT) consists of a network of interconnected devices, sensors, and systems that communicate and exchange data to perform various tasks and provide valuable insights. IoT devices are deployed across various sectors, including smart homes, industrial automation, healthcare, transportation, and more. Impact of Edge Computing on IoT Improved Response Times One of the primary benefits of edge computing in IoT is the significant improvement in response times. By processing data closer to the source, edge computing reduces latency, which is the time it takes for data to travel from the

Data structures are fundamental concepts in computer science that help organize and store data efficiently. In the context of AI and machine learning, understanding data structures is crucial because these fields often deal with large volumes of data that need to be processed and analyzed quickly. Here's a starter guide to some key data structures relevant to AI and machine learning: Arrays : Arrays are one of the simplest data structures, consisting of a collection of elements stored in contiguous memory locations. In AI and machine learning, arrays are often used to represent datasets, input features, or output predictions. Lists : Lists are similar to arrays but more flexible because they can dynamically resize. In Python, for example, lists can grow or shrink as needed, making them useful for managing datasets of varying lengths. Stacks : Stacks follow the Last In, First Out (LIFO) principle, where the last element added is the first one to be removed. Stacks are commonly used