Deep Learning vs. Machine Learning: What is the Difference

Artificial Intelligence is developing at a rapid pace ranging from advancements in the sector of self-driving vehicles and the ability to outsmart humans in such games as poker and Go to automated customer service. This technology is at the forefront of innovations and optimization in the business sphere and stands tall to launch a digital revolution.

Even now, Artificial Intelligence, Machine Learning, and Deep Learning are an integral part of many enterprises. These terms often seem to be interchangeable buzzwords, but we surely know there is a difference between Machine Learning and Deep Learning. Let’s do some research!

What is Machine Learning after all?

The Internet is flooded with articles that can explain to you in plain English all the peculiarities of the amazing notion of Machine Learning. Good examples are here and here. Let’s just see below what types of ML exist and when to use Machine Learning.

The main principle here is that machines obtain data and “learn” from it. Currently, it is the most promising AI-based tool for business. Machine Learning systems allow us to quickly apply knowledge obtained during the learning process with the use of big data sets which makes it possible to demonstrate advancements in such tasks as face recognition, speech recognition, object recognition, translation, and many others. Contrary to programs with manually encoded instructions on how to perform certain tasks, Machine Learning enables the system to learn for itself how to recognize patterns and make prognoses.

Deep Learning and Where to Find It

If we set off Deep Learning vs. Machine Learning vs. Artificial Intelligence, we may see that relations between these three notions are pretty much the same as shown in the title image: one is a component part of the other.

From the practical point of view, Deep Learning is a subset of Machine Learning, that is why many technical experts would agree that Deep Learning is always Machine Learning. However, their capabilities differ.

Deep Learning can cost you a fortune and requires large data sets for its training. We may explain it by the fact that there are many parameters that require adjusting for training algorithms to avoid false triggering.

For example, a Deep Learning algorithm was given a task to “recognize” how a cat looks like. To perform training, you’ll need thousands of pictures to teach it how to differentiate between the smallest details which differentiate a cat from, say, a cheetah or a panther, or a fox.

Or to put it very simply, it is something like that.

Deep Learning is also applied in businesses. We may utilize a large amount of data — millions of pictures — and by using them detect certain characteristics. Text search, fraud detection, spam detection, manual text input detection, image search, speech recognition, translation — all these tasks can be done by smartly trained Deep Learning. For example, Google replaced many systems that were based on rules and required manual input with deep learning networks.

What Is the Difference Between Deep Learning and Machine Learning?

The most important difference between the two lies in the ability to process unstructured data using artificial neural networks (ANN), since Deep Learning is capable of transforming unstructured information like texts, images, sounds, and videos into numerical values. This extracted information is then used for pattern recognition or for further learning.

Artificial Machine Learning, even with the use of the decision tree procedure, is impotent when it comes to processing such unstructured data. That is why, for example, images cannot be used that much simply as input data for learning an algorithm how to recognize objects. This case requires costly Feature Engineering and a human who would control this process.

More information on Machine Learning and Deep Learning differences are in the table below.

Is There Anything Left for Humans?

Well, what else does a human have to do in the process? We think that professional expertise and designing data sets used for problem creation and problem-solving are important tasks that are still left for humans.

But it is obvious that many tech giants spend extremely huge amounts for the automation of Machine Learning processes. For example, Google, Amazon Sagemaker, H2O, and many others have come with an AutoML platform. This tool was brought to life with the idea that in the future considerably more processes will be automated and more and more humans will vanish from this process.

Is your business interested in integrating Machine Learning or Deep Learning into your strategy? Contact us now and let us know everything about your ideas.