• Define Aprendizaje Significativo
• Define Aprendizaje Social
• Define Aprendizajes Clave

Referencias • • • • • • • • • • https://www.verywell.com/learning-theories-in-psychology-an-overview-2795082.

Define Aprendizaje Significativo

Define Aprendizaje Social

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Define Aprendizajes Clave

Deep Learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world. This data, known simply as, is drawn from sources like social media, internet search engines, e-commerce platforms, online cinemas and more. This enormous amount of data is readily accessible and can be shared through fintech applications like. However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information. Companies realize the incredible potential that can result from unraveling this wealth of information, and are increasingly adapting to Artificial Intelligence (AI) systems for automated support. One of the most common AI techniques used for processing Big Data is, a self-adaptive algorithm that gets increasingly better analysis and patterns with experience or with new added data.

If a digital payments company wanted to detect the occurrence of or potential for fraud in its system, it could employ machine learning tools for this purpose. The computational algorithm built into a computer model will process all transactions happening on the digital platform, find patterns in the data set and point out any anomaly detected by the pattern. Deep learning, a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a nonlinear approach. A traditional approach to detecting fraud or might rely on the amount of transaction that ensues, while a deep learning nonlinear technique would include time, geographic location, IP address, type of retailer and any other feature that is likely to point to a fraudulent activity.