Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers.
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. Deep learning algorithms are used, especially when we have a huge no of inputs and outputs.
Deep 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 is a collection of statistical techniques of machine learning for learning feature hierarchies that are actually based on artificial neural networks.
So basically, deep learning is implemented by the help of deep networks, which are nothing but neural networks with multiple hidden layers.
Architectures:
- Deep Neural Networks (DNN)
It is a neural network that incorporates the complexity of certain level, which means several numbers of hidden layers are encompassed in between the input and output layers.
- Deep Belief Networks (DBN)
A deep belief network is a class of Deep Neural Network that comprises of multi-layer belief networks.
- Recurrent Neural Networks (RNN)
It permits parallel as well as sequential computation and it is exactly similar to that of the human brain.
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