Deep Learning vs. Machine Learning – What’s The Difference?

To most people, the terms Deep Learning and Machine Learning seem like interchangeable buzzwords in the AI world. However, that’s not true. Hence, everyone who seeks to better understand the field of Artificial Intelligence should begin by understanding the terms and their differences. The good news: It’s not as difficult as some articles on the topic suggest.

What's the difference between Deep Learning and Machine Learning?

Machine Learning means computers learning from data using algorithms to perform a task without being explicitly programmed. Deep Learning uses a complex structure of algorithms modeled on the human brain. This enables the processing of unstructured data such as documents, images, and text.

To break it down in a single sentence: Deep Learning is a specialized subset of Machine Learning which, in turn, is a subset of Artificial Intelligence. In other words, Deep Learning is Machine Learning.

What is Machine Learning?

Machine Learning is the general term for when computers learn from data. It describes the intersection of computer science and statistics where algorithms are used to perform a specific task without being explicitly programmed; instead, they recognize patterns in the data and make predictions once new data arrives.

In general, the learning process of these algorithms can either be supervised or unsupervised, depending on the data being used to feed the algorithms. If you want to dive in a little bit deeper into the differences between supervised and unsupervised learning have a read through this article.

A traditional Machine Learning algorithm can be something as simple as linear regression. For instance, imagine you want to predict your income given your years of higher education. In the first step, you have to define a function, e.g. income = y + x * years of education. Then, give your algorithm a set of training data. This could be a simple table with data on some people’s years of higher education and their associated income. Next, let your algorithm draw the line, e.g. through an ordinary least squares (OLS) regression. Now, you can give the algorithm some test data, e.g. your personal years of higher education, and let it predict your income.

While this example sounds simple it does count as Machine Learning – and yes, the driving force behind Machine Learning is ordinary statistics. The algorithm learned to make a prediction without being explicitly programmed, only based on patterns and inference.

So much about Machine Learning in general – to summarize:

• Machine Learning is at the intersection of computer science and statistics through which computers receive the ability to learn without being explicitly programmed.
• There are two broad categories of Machine Learning problems: supervised and unsupervised learning.
• A Machine Learning algorithm can be something as simple as an OLS regression.

Let's now examine how the term Deep Learning relates to all of this.

What is Deep Learning?

Deep Learning algorithms can be regarded both as a sophisticated and mathematically complex evolution of machine learning algorithms. The field has been getting lots of attention lately and for good reason: Recent developments have led to results that were not thought to be possible before.

Deep Learning describes algorithms that analyze data with a logical structure similar to how a human would draw conclusions. Note that this can happen both through supervised and unsupervised learning. To achieve this, Deep Learning applications use a layered structure of algorithms called an artificial neural network (ANN). The design of such an ANN is inspired by the biological neural network of the human brain, leading to a process of learning that’s far more capable than that of standard machine learning models.

Consider the example ANN in the image above. The leftmost layer is called the input layer, the rightmost layer of the output layer. The middle layers are called hidden layers because their values aren't observable in the training set. In simple terms, hidden layers are calculated values used by the network to do its "magic". The more hidden layers a network has between the input and output layer, the deeper it is. In general, any ANN with two or more hidden layers is referred to as a deep neural network.

Today, Deep Learning is used in many fields. In automated driving, for instance, Deep Learning is used to detect objects, such as STOP signs or pedestrians. The military uses Deep Learning to identify objects from satellites, e.g. to discover safe or unsafe zones for its troops. Of course, the consumer electronics industry is full of Deep Learning, too. Home assistance devices such as Amazon Alexa, for example, rely on Deep Learning algorithms to respond to your voice and know your preferences.

How about a more concrete example? Imagine the company Tesla using a Deep Learning algorithm for its cars to recognize STOP signs. In the first step, the ANN would identify the relevant properties of the STOP sign, also called features. Features may be specific structures in the inputted image, such as points, edges, or objects. While a software engineer would have to select the relevant features in a more traditional Machine Learning algorithm, the ANN is capable of automatic feature engineering. The first hidden layer might learn how to detect edges, the next is how to differentiate colors, and the last learn how to detect more complex shapes catered specifically to the shape of the object we are trying to recognize. When fed with training data, the Deep Learning algorithms would eventually learn from their own errors whether the prediction was good, or whether it needs to adjust.