Machine Learning and Deep Learning: What is the Difference?
As of today, solutions exploiting artificial intelligence are extensively used not only for business needs but also for attaining various non-business objectives. They are based on two concepts: machine learning and deep learning.
Both terms are not new, but not everyone comprehends the differences between them. Let us have a closer look at these approaches and also consider the advantages peculiar to each of them.
The Key Differences between Machine Learning and Deep Learning
Machine Learning is a branch of artificial intelligence based on the idea that the computer does not just use prewritten algorithms but learns by itself how to solve problems.
This is a whole subject area implying a number of methods and algorithms to determine a trend or develop an action pattern.
The machine learning objective is to predict the result based on input data. The more varied the input data, the easier it is for the machine to find patterns and the more accurate the result will be.
Deep Learning is a class of machine learning algorithms. It resorts to a multi-layered filter system to hierarchically extract useful features. This means that each successive layer of the input receives the output data of the previous layer. The features of a higher level are derived from the features of a lower level.
Most deep learning algorithms are types of neural networks. The neural network is a learning system, meaning that it acts according to both predetermined algorithms and its own experience. Artificial neural networks are based on human brain biology. They imitate the way humans think.
For traditional machine learning algorithms to operate successfully, such a stage of data preprocessing as feature engineering is very important. This is a process used to form and select features.
The fundamental difference between deep learning and machine learning is that the former is able to perform most of the work necessary to form features using only consistently presented input data without the need for complex features selected manually.
Advantages of Deep Learning
Deep learning can be presented as a cascade of layers. Each successive layer uses the output signal from the previous layer as input data. This feature and a number of other features provide certain advantages when applied to solving a variety of problems.
1. High-level Performance
In many areas like computer vision, speech recognition, and natural language processing, neural networks based on deep learning technologies are currently many times superior to the methods used by classical machine learning. The accuracy level increases while the number of errors decreases.
2. Ability to Develop New Functions
Classical machine learning presumes that humans develop functions, and that that approach is very time-consuming. Deep learning is capable of generating new functions based on the limited number available in their learning data set. What we mean is that deep learning algorithms can create new tasks to reach current objectives.
3. Advanced Analysis Capabilities
To make machine learning algorithms operate correctly, it is necessary to prepare labeled data. The system based on deep learning algorithms is capable of becoming “smarter” by itself in the process of problem solving and can work with unlabeled data.
4. Adaptability and Scalability
Deep learning methods are much easier to adapt to different areas, in comparison to classical ML algorithms. This feature becomes possible due to transfer learning facilitating in which the entire model is learned, in most cases, helping to achieve higher productivity in a shorter period of time.
Another important advantage is scalability. Neural networks can handle data growth better than classical machine learning algorithms. It is well demonstrated in the chart provided below.
If we talk about the disadvantages of deep learning, the main problems are the higher implementation costs (hardware requirements) and some difficulties in algorithm interpretation.
In the context of business, artificial intelligence is now massively implemented both in business processes and in products meant for users.
Deep learning technologies are used to solve a wide range of tasks in various niches. The most well-known area where they are applied is image processing. For example, based on deep learning, the Pinterest recommendation algorithm operates, and Facebook “recognizes” users in photos.
Currently, engineers have taught neural networks to recognize tumors on X-ray images, play Go and chess, and create music and lyrics. However, deep learning capabilities are far from being limited only to these areas.
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