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Overview: Data Science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. This third edition delves into the latest advancements in AI, particularly focusing on large language models (LLMs), with clear distinctions made between AI and traditional data science, including AI’s ability to emulate human decision-making. Artificial Intelligence (AI) is an umbrella term that captures an extraordinary field of computer science dedicated to creating systems capable of tasks that, until recently, required human intelligence. Today, AI stands at the forefront of innovation. The development of advanced algorithms and the exponential increase in computational power have catapulted AI from academic papers to real-world applications. Machine Learning (ML), a cornerstone of contemporary AI, is predicated on the principle that systems can learn from data, identify patterns, and make decisions with minimal human intervention. This paradigm shift from explicit programming to data-driven learning has been pivotal to AI’s success. By using algorithms that iteratively learn from data, ML has enabled AI to develop predictive models that are integral to a myriad of applications, from email filtering to sophisticated diagnostics in healthcare. Deep Learning, a subset of ML, has taken inspiration from the information processing patterns found in the human brain. It utilizes algorithms called neural networks which consist of layered structures resembling neurons. These networks can learn and make intelligent decisions on their own. Deep Learning has been fundamental in advancing AI, enabling machines to recognize images, decipher spoken words, and even generate human-like text.
Genre: Non-Fiction > Tech & Devices
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