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Deep Learning is a subset of Machine Learning, which in turn, is a subset of Artificial Intelligence.Artificial IntelligenceArtificial Intelligence (AI) is a technique that helps machines to mimic human behavior.Machine LearningMachine Learning is an application of AI that allows the system to learn and improve from experience automatically.Deep LearningDeep Learning is a type of Machine Learning that is inspired by the structure ofthe brain. It is also known as Artificial Neural Network (ANN). It uses complex algorithms and deep neural networks to train models.What is Deep Learning?DefinitionDeep Learning involves networks which are capable of learning from data and functions similar to the human brain.Why Deep Learning?Processes massive amount of dataDeep Learning can process an enormous amount of both Structured and Unstructureddata.Performs Complex OperationsDeep Learning algorithms are capable enough to perform complex operations when compared to the Machine Learning algorithms.Achieves Best PerformanceAs the amount of data increases, the performance of Machine Learning algorithms decreases.On the other hand, Deep Learning maintains the performance of the model.Feature ExtractionMachine Learning algorithms extract patterns from labeled sample data, while Deep Learning algorithms take large volumes of data as input, analyze them to extract the features on its own.Machine LearningIf done through Machine Learning, we need to specify the features based on whichthe two can be differentiated like size and stem, in this case.Deep LearningIn Deep Learning, the features are picked by the Neural Network without any human intervention. But, that kind of independence can be achieved by a higher volume of data in training the machine.Neural NetworksThe human brain contains billions of cells called Neurons. The structure of a neuron is depicted in the above image.Neural Networks is a set of algorithms designed to learn the way our brain works.The biological neurons inspire the structure and the functions of the neural networks.Biological and Artificial Neurons - TerminologiesBiological NeuronArtificial NeuronDendritesInputsNucleusNodesSynapseWeightsAxonOutput
A Node is also called a Neuron or Perceptron.The basic structure of an Artificial Neural Network (ANN) consists of artificialneuron that are grouped into 3 different layers namely:There are three different layers in a neural network, namely:Input LayerHidden LayerOutput LayerInput LayerThe input layer communicates with the external environment and presents a pattern to the neural network.