A deep neural network (DNN) is a type of artificial neural network that contains multiple layers of interconnected neurons, allowing it to learn and model complex patterns in data. DNNs are a fundamental component of deep learning, a subset of machine learning focused on training models with multiple layers to automatically learn hierarchical representations of data. Convolutional Neural Networks (CNNs) are specialized deep neural networks commonly used for image-related tasks, while Recurrent Neural Networks (RNNs) are well-suited for sequential data, such as language and time series data.
The term "deep" in deep neural networks refers to the depth of the network, which is determined by the number of hidden layers between the input layer and the output layer. Unlike shallow neural networks with just one or two hidden layers, deep neural networks typically have several hidden layers, which enables them to learn more abstract and intricate features from the input data.
Key characteristics…
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Multiple Hidden Layers: DNNs consist of multiple hidden layers, each containing multiple neurons. Each hidden layer learns to represent different levels of abstraction in the data.
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Hierarchical Learning: Deep neural networks learn hierarchical representations of data. Lower layers capture simple and low-level features, while higher layers learn more abstract and complex features.
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Non-Linearity: Activation functions introduce non-linearity into the model, allowing DNNs to learn and model complex, non-linear relationships in the data.
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Automatic Feature Extraction: DNNs automatically learn features from the data during training, reducing the need for manual feature engineering.