DL Series: What is Deep Learning?

Part I

One of the subfields of machine learning (ML), deep learning (DL) is focused on artificial neural networks, which are inspired by the structure and function of the human brain. Deep learning attempts to mimic the brain by enabling systems to cluster data and make extremely accurate predictions. With that said, these artificial neural networks are still a far ways from achieving the same level of computation as their human counterpart. One of the reasons for the divide is that deep learning requires orders of magnitude more data to work.

Deep Learning vs. Machine Learning

Before fully understanding deep learning, it is first important to distinguish it from machine learning. There are a few key differences between the two.

Machine learning often requires more human intervention to produce results, such as labeling and classifying input data. On the other hand, deep learning systems can work with much larger datasets and feature set sizes, even if they are unlabeled. Deep learning systems also require far more powerful hardware and resources, taking more time to set up. One more key difference between the two is that deep learning requires more expertise to carry out tasks like neural network architecture selection and hyperparameter optimization.