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Artificial intelligence (AI) has suffered through decades of unrealistic expectations followed by disappointment and subsequent changes in research direction. These AI winters were necessary to alter the direction of research to more promising ideas, resulting in an AI spring.
Deep learning came about roughly 20 years ago. It built on the ideas of neural networks by increasing their scope, basic algorithms, and their depth. The introduction of deep neural networks has created a revolution that powers self-driving vehicles, giving machines the ability to describe the contents of images, and much more. This article explores the origins of deep learning, the architectures of deep neural networks, and the current state-of-the-art use cases. It also introduces the frameworks used to create deep neural networks that are accessible to anyone.
What Is Deep Learning?
Deep learning is a subfield of machine learning (ML) and represents a set of neural network architectures that solves complex, cutting-edge problems. These architectures (or models) go by the names convolutional neural networks (CNNs) and long short-term memory (LSTM), among others...
Deep Learning vs. Neural Networks
A key differentiating feature of deep neural networks is depth: the number of layers in addition to the breadth or number of processing elements within each layer. But, deep neural networks have evolved from the typical multilayer networks that brought us to the point of deep learning. The architectures of deep neural networks are different from their multilayer ancestors. CNNs, which work well with image data, sample and pool pixels from the image for processing. RNNs, which are ideal for sequence data such as text, consider not just an input but the inputs that precede and follow it...
Conclusion
As a set of architectures that are advancing the state of the art in many application areas, deep learning has brought a resurgence of interest in ML. Peripheral industries have also taken note, and hardware and software technologies are advancing to bring deep learning to new classes of devices and applications. Deep learning frameworks are also making these architectures more accessible and popular.
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Source: Toolbox
