The main advantage of a CNN over a standard neural network is that the former is able to learn visual and spatial features of the input, while for the latter such information is lost due to flattening input data into a vector. CNNs have made significant strides in the field of computer vision, starting with increased classification accuracies of MNIST data and object recognition, semantic segmentation, and other domains. CNNs have many applications in real life, from facial detection in social media to autonomous vehicles. Recent approaches have also applied CNNs to natural language processing and text classification tasks to produce state-of-the-art results.
Now that we have covered the basics of machine learning, ...
