No. In all supervised learning, the algorithm learns a model from the data and generalise it for unseen data. Better generalisation means better model. In unsupervised learning, (mostly clustering, density estimation etc) the same thing happens but we don't tell the algorithm what to learn.
Deep learning is not machine learning plus something else. It is a collection of techniques that overcomes the scalability problem of feed forward neural networks. NNs are very difficult to scale over number of layers. Standard training method of back propagation can't handle many layers because of vanishing gradient and the computational infeasibility brought on by the explosive growth of connections.
NNs are very difficult to scale with additional classification targets you may require (for example, you have a classifier for categorising 10 classes, but to scale it up to 20, requires a lot of topological changes and qualitative analysis.)
Deep learning addresses the scaling over layers with various techniques coupled with hardware acceleration (GPUs). Currently this stand at about 150 layers.
Deep learning is not machine learning plus something else. It is a collection of techniques that overcomes the scalability problem of feed forward neural networks. NNs are very difficult to scale over number of layers. Standard training method of back propagation can't handle many layers because of vanishing gradient and the computational infeasibility brought on by the explosive growth of connections.
NNs are very difficult to scale with additional classification targets you may require (for example, you have a classifier for categorising 10 classes, but to scale it up to 20, requires a lot of topological changes and qualitative analysis.)
Deep learning addresses the scaling over layers with various techniques coupled with hardware acceleration (GPUs). Currently this stand at about 150 layers.