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Текст статьи: https://blogs.oracle.com/bigdata/neural-networks-in-deep-learning

Peter Jeffcock, Neural Networks in Deep Learning, Oracle, 2020.

Neural Networks in Deep Learning.

The aim of the article is to give the reader a basic understanding of neural networks: what they are, how they work, and where they are used.

The research method is description.

The author gives a brief description of how neurons “fire”, what causes it and what the output might be.

One of the basic example tasks that may be solved with neural networks is recognition of handwritten digits. A network with one hidden layer can be used to determine the number on a 28x28 pixels image. However, this can’t be considered a deep learning as it requires at least 2 hidden layers.

The author states that more complex image recognition tasks like determining the number of specific objects in an image have to use a different approach. Instead of focusing on the whole picture and trying to figure out shapes that compose it, we need to isolate and analyze specific areas that correspond to items we are tying to identify the way human brain does it. Convolutional neural networks do this kind of processing by splitting an image into equal overlapping parts and applying special logic to them. This type of network is on the forefront of image recognition.

Speech processing, whether it is spoken or written, is a much harder task, as language is highly contextual. That’s why networks with some form of feedback loop is needed. The author describes yet another type of neural network called recurrent neural networks and long short-term memory units that comprise it.

The article describes the period …

The author’s conclusion is that many approaches to neural networks have their own strengths and weaknesses and are used to solve different types of problems.

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