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Table Of Contents
Machine Learning For Dummies
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After backpropagation, the next improvement in neural networks led to deep learning. Research continued in spite of AI winter and neural networks started to take advantage of the developments in CPUs and GPUs (the graphic processing units better known for their application in gaming but which are actually powerful computing units for matrix and vector calculations). These technologies make training neural networks an achievable task in a shorter time and accessible to more people. Research also opened a world of new applications. Neural networks can learn from huge amounts of data, and because they’re more prone to high variance than to bias, they can take advantage of big data, creating models that continuously perform better, depending on the amounts of data you feed them. However, you need large, complex networks for certain applications (to learn complex features, such as the characteristics of a series of images) and thus incur problems like the vanishing...
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