Deep Neural Network 편집하기 (부분)

==Relationship with Multi-Layer Perceptron (MLP)==
Deep Neural Networks and Multi-Layer Perceptrons share foundational principles but differ in depth and applications:
*'''Commonality''': Both DNNs and MLPs are composed of layers of interconnected neurons. Each neuron processes data inputs and applies activation functions, enabling both models to learn patterns in data.
*'''Depth Difference''': An MLP typically has one or a few hidden layers, while a DNN has many hidden layers, often more than three. This depth allows DNNs to learn more complex, hierarchical representations that MLPs cannot.
*'''Capability''': MLPs are best suited for simpler, structured tasks, whereas DNNs excel in more complex applications (e.g., image and text processing) due to their depth and ability to learn intricate features.
*'''Activation Functions''': Both can use various activation functions (like ReLU, sigmoid, or tanh), but DNNs frequently rely on non-linear activations (e.g., ReLU) that are well-suited for deep architectures.
In essence, DNNs can be considered as deep versions of MLPs, where the increased depth enables learning from high-dimensional and unstructured data, something basic MLPs may struggle with.