Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?
A . Use K-means Clustering to detect faces in the pixels.
B . Use feature engineering to add features for eyes, noses, and mouths to the input data.
C . Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
D . Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

Answer: C

Explanation:

Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as “deep” learning. So deep is a strictly defined, technical term that means more than one hidden layer.

In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer’s output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer. A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn’t be able to "build" these features using previous hidden layers that detect low-level features, such as lines. Feature engineering is difficult to perform on raw image data. K-means Clustering is an unsupervised learning method used to categorize unlabeled data.

Reference: https://deeplearning4j.org/neuralnet-overview