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The basic architecture consists of an input layer, several hidden layers of interconnected neurons, and an output layer that delivers the final decision or classification. Non-linearities introduced in these layers enable the model to express intricate relationships, crucial for handling the variability present in data.
Successes of Modern Deep Learning
Deep learning has not only matured but excelled in various applications. A milestone in its evolution occurred in 2012 when convolutional neural networks (CNNs) dominated the ImageNet challenge, setting a precedent for what deep learning could achieve. The model involved a 12-layer architecture with millions of parameters trained on vast datasets, showcasing significant advances in object recognition.