![]() It is a neural network that incorporates the complexity of a certain level, which means several numbers of hidden layers are encompassed in between the input and output layers. So, as and when the hidden layers increase, we are able to solve complex problems. Likewise, more hidden layers can be added to solve more complex problems, for example, if you want to find out a particular kind of face having large or light complexions. So, in the 2 nd hidden layer, it will actually determine the correct face here as it can be seen in the above image, after which it will be sent to the output layer. And then, it will fixate those face features on the correct face template. Then the 1st hidden layer will determine the face feature, i.e., it will fixate on eyes, nose, and lips, etc. After then, these input layer will determine the patterns of local contrast that means it will differentiate on the basis of colors, luminosity, etc. In the example given above, we provide the raw data of images to the first layer of the input layer. ![]() So basically, deep learning is implemented by the help of deep networks, which are nothing but neural networks with multiple hidden layers. Deep learning is a collection of statistical techniques of machine learning for learning feature hierarchies that are actually based on artificial neural networks. Since deep learning has been evolved by the machine learning, which itself is a subset of artificial intelligence and as the idea behind the artificial intelligence is to mimic the human behavior, so same is "the idea of deep learning to build such algorithm that can mimic the brain".ĭeep learning is implemented with the help of Neural Networks, and the idea behind the motivation of Neural Network is the biological neurons, which is nothing but a brain cell. Deep learning algorithms are used, especially when we have a huge no of inputs and outputs. The output from each preceding layer is taken as input by each one of the successive layers.ĭeep learning models are capable enough to focus on the accurate features themselves by requiring a little guidance from the programmer and are very helpful in solving out the problem of dimensionality. Basically, it is a machine learning class that makes use of numerous nonlinear processing units so as to perform feature extraction as well as transformation. In deep learning, nothing is programmed explicitly. Since neural networks imitate the human brain and so deep learning will do. Deep learning is based on the branch of machine learning, which is a subset of artificial intelligence.
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