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In traditional programming, the focus is on using rules and data to find answers. This is typically represented as rules + data = answers. In contrast, AI/ML takes a different approach: Answers + data = rules. In AI/ML, we train models by providing large amounts of data and demonstrating how humans solve the problems within that data. This helps the AI learn and develop new rules to respond to the data similarly to human intelligence. The more experiences (data) the AI is exposed to, the better it becomes at navigating future situations. Essentially, training a model is akin to feeding it experiences and allowing it to learn from them. โจ Follow me for more useful tech information.
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Final Year CS Student | Exploring Opportunities in Generative AI, Data Science & ML Engineering Iโm currently in my final year of Computer Science and Business Systems and actively seeking internship opportunities where I can apply my skills in AI,
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Machine Learning vs. Deep Learning: Whatโs the Real Difference? ๐คโก Machine Learning (ML) and Deep Learning (DL) are both AI-driven, but theyโre not the same! While ML relies on algorithms to learn from data, DL uses artificial neural networks to pr
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