Applications of deep neural networks
oduntan haydex2019, Oduntan Adeola
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Abstract
ABSTRACT A Deep Neural Network is an artificial neural network with multiple layers between the input and output layers. The architecture is inspired by the hierarchical structure of the brain. Deep neural networks feature a hierarchical, layer-wise arrangement of non-linear activation functions called neurons, fed by inputs into the network. Deep Neural Networks are typically feed-forward networks in which data flows from the input layer to the output layer without looping back. The term ‘deep’ refers to the number of hidden layers in the neural networks, while neural networks have two to three hidden layers, deep neural networks can have as many as thousands hidden layers (Nataniel K. and Jeff Brondy). The purpose of implementing a deep neural network is to find a transformation of data for making a decision. They serves as a quick methods to build classification and regression models that are very difficult to program. Some of the techniques that allow deep neural networks to solve problems are back propagation, which computes the partial derivatives of a function, Dropout for correcting the problems associated with over-fitting by combining the predictions of different large neural networks at test time, Max-pooling, Batch Normalization, Long Short-term Memory (LSTM), Transfer Learning, Continuous Bags of Words e.t.c. Deep neural networks have been applied to numerous fields including Computer vision, Speech recognition, Natural language processing (NLP), Audio recognition, Social network filtering, Machine translation, Bio-informatics, Drug-design, Medical image analysis and board game programs, where they have produced results comparable to human experts and in some cases superior to human experts (Karen Simonyan, 2014). The industries and areas to which deep neural networks can be applied to in the future are categorized into health, Agriculture, Banking, Multimedia e.t.c., on the other hand, it will serve numerous industries in technology roles such as reducing the lags and bandwidth bottlenecking that results from the internet plurality of media contents.
Key takeaways
- Some examples of neural networks that exist include:
- Artificial Neural Networks are the computational models inspired by the human brain.
- Deep neural network is an Artificial Neural Network with multiple layers between the input and output layers.
- This allows it to exhibit temporal dynamic behavior, unlike artificial neural networks, recurrent neural networks can use their internal state (memory) to process sequence of inputs.
- All in all, neural networks have made computer systems more useful by making them more human.
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