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Deep Learning vs Machine learning

Most find the concept of AI confusing, more so with its subsets, machine learning and deep learning. Though these terms may seem interchangeable at first, there is a certain nuance that differentiates them from each other. In this article, we break down what machine learning and deep learning really are and the differences between the two.

Artificial Intelligence

Before we dive into the two terms, it’s important to understand Artificial Intelligence as a whole, which in short explains the logic behind these algorithms. Artificial Intelligence, or AI, is a science that is primarily concerned with creating machines and programs that can think and act like human beings. Though the concept may seem easy to understand, its application is quite complex. Trying to reach the complexities of human intelligence has been an arduous task, especially considering the fact that computers base their logic in defined rules, whereas human logic at times defies those same rules. Though current technology cannot match the speed and logic involved in everyday complex decisions, machine learning and deep learning are subsets that are a step closer to understanding these decisions and predictions with limited human intervention.

Machine Learning

The concept of machine learning lies in a computer’s ability to actively learn from data with the help of algorithms to perform a task that isn’t directly programmed into the code. It derives its decisions with the help of both computer science and statistics, recognizing patterns present in the data and creating predictions accordingly. An interesting example of a machine learning algorithm is Spotify. The algorithm in the app determines what music or artists a user might like based on previous listens that become the base of the user’s general preference. The algorithm uses these preferences and compares it to other users who have the same preferences, then suggesting songs based on what these users with similar music taste have actively listened to. This form of machine learning is popularly used in a multitude of services that offer automated recommendations. A positive aspect of machine learning is that, like the name suggests, it is constantly learning. Even if the predictions are not accurate in the beginning, the data based on continuous choices and comparison to similar users serves as a tool for further learning in the algorithm and helps it progressively get better over time.

Deep Learning

Deep Learning can be defined as a more complex evolution of machine learning algorithms. The aim of a deep learning algorithm is to analyze data and make logical decisions similar to how a human being would make decisions. To achieve this, deep learning applications generally use a layered structure of algorithms called an artificial neural network. Inspired by the neural network of the human brain, there are a multitude of layers with commands hidden in the set that help with creating a deep neural network that can accurately make the correct decisions. It makes its decisions with the help of a large amount of data, with little to no human intervention required. A great example of a deep learning algorithm is Tesla’s object programming. The code in every Tesla is modeled to detect various objects like stop signs and pedestrians. If there is a detectable object, the car slows down, similar to the action a human driver would make. This makes deep learning an interesting evolution that is one step closer to understanding human logic and rational decision-making.

Even if there are differences between the two algorithms, it’s accurate to see them as part of each other. Deep learning algorithms are machine learning algorithms that are more evolved to make accurate predictions about complex situations. Deep learning, though more complex, involves less human intervention than regular machine learning programs and takes in more data than it as well. Many of today’s applications and softwares take advantage of both algorithms to perform actions and predictions that benefit the user greatly. By making reliable predictions, they have become inseparable from the online experience, and their continuous evolution makes it so that they only have room to grow.

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