Deep learning has become an ever-growing part of the Machine Learning family. Part of its growth can be accredited to its compatibility with Python, a high-level programming language that’s also been rising in popularity since its creation in 1991.
While you can pick from a variety of languages to use with Deep learning — such as C++, Java, and LISP — Python remains the first choice for millions of developers worldwide. The powerful open-source TensorFlow and PyTorch libraries (developed by Google and Facebook, respectively) provide a straightforward interface for Python developers to build neural networks.
In order to understand both the differences between deep learning and machine learning, you first need to dip your toes into the concept of Artificial Intelligence (AI).
AI is the science of simulating human Intelligence in machines, allowing them to recognize patterns, analyze situations, and come up with suitable reactions. However, AI is a broad concept. If a machine can ‘think’, then it’s an AI. How you achieve the desired level of Intelligence is where the various subsets of AI come into play.
Machine learning, a subset of AI, is a learning process in which structured data is fed to a machine. Without machine learning, the developer would have to manually program and implement all the needed algorithms and rules the AI system would need to function.
The success of a machine learning operation relies primarily on two factors: quality and quantity of the data. The structured data needs to be accurate and to prioritize one characteristic that you want the machine to learn to recognize. As for quantity, the more variants of the same type of data the machine received, the better its understanding of the subject matter.
How the machine learns is divided into multiple categories depending on how the data is presented and whether the machine received direct or excessive assistance from humans.
Supervised learning consists of feeding computers training data along with human participation to help train it on how to respond to said data. Semi-supervised learning gives the machines a level of independence by supplying them with a mixture of labeled data points along with unlabeled, but related, data. The computer is then required to identify the unlabeled objects according to what it 'thinks' is correct.
However, those two methods are limiting. They require massive volumes of data, labeled and unlabeled, as well as consistent and reliable human participation, which makes working on bigger projects a challenge. Other subsets of machine learning are different. They convert the majority of the work towards the machine, only involving humans in the early stages of the build.
Unsupervised learning — and its subset, self-supervised learning — as the name suggests, feeds computers unlabeled data. This practice gives the machines complete freedom to find patterns and categorize objects and data points accordingly.
While machine learning is the practice of teaching computers to think like people, deep learning is one of the methodologies. It cannot be separated from machine learning as a whole. By applying any of the previously mentioned machine learning approaches, the machine still thinks like a computer running a step-by-step program, albeit a complex one.
Some examples of machine learning algorithms that would not be considered deep learning are linear regression, random forests, and k-nearest neighbors. Of these, k-nearest neighbors is probably the most intuitive; essentially it is following the real estate practice of finding “comps” (other data points that are similar) and giving an answer based on averaging those values. You can see how this is teaching a computer to “think”, but only in a narrow, limited way.
Deep learning, on the other hand, is a subset of machine learning that applies neural networks rather than simple algorithms.
