A Comparison of Machine Learning and Deep Learning

A Comparison of Machine Learning and Deep Learning

In recent years, the concept of artificial intelligence has become increasingly popular. Whether people fully understand it or not, they often come across terms such as artificial intelligence, machine learning, and deep learning. So, what exactly are machine learning and deep learning, and how are they related to artificial intelligence? Although “machine learning” and “deep learning” may sound like synonyms, and both are concrete approaches to implementing artificial intelligence, there are still some important differences between them.

Machine Learning

Machine learning is a subset of artificial intelligence. It uses statistical techniques to give computers the ability to “learn” from data without requiring complex programming. Simply put, machine learning can be defined as a field of study that enables computers to act and learn in a human-like way, improving their performance independently through information and data provided in the form of real interactions and observations. Machine learning supports automation across many industries and tasks, from malware analysis and data security to financial professionals seeking profitable trades.

Let’s take the example of a well-known music streaming service that must decide which new artist or song to recommend to a listener. Machine learning algorithms help identify other listeners with similar tastes. In this case, machine learning acts as a virtual assistant, providing insight into new preferences and trends in the music industry, and the system can use this information to recommend new songs to listeners.

Deep Learning

Unlike task-specific algorithms, deep learning is a subset of machine learning that is based on learning from data. It is inspired by the function and structure of what are known as artificial neural networks. Deep learning gains enormous flexibility and power by representing the world as simpler concepts arranged in hierarchies, and by building more abstract representations from less abstract ones. Although the term “deep learning” has been around for several years, it is now receiving more and more attention and widespread publicity.

To understand this concept, consider the example of an animal recognizer that helps determine whether a given image is a lion or a deer. If we treat this as a traditional machine learning problem, we would work with specific features, such as whether the animal has ears, whiskers, or other visible parts. In simple terms, we would define facial and physical features for the system so it could identify the animal. In deep learning, however, the process begins differently. Deep learning automatically defines and classifies the key features. It first determines the most relevant factors for distinguishing a lion from a deer. It then begins identifying combinations of shapes and edges in order to recognize the object more deeply—for example, whether it has ears or whiskers. After this continuous, layered process of recognizing such concepts, it determines which features are responsible for producing the correct answer.

When Should You Use Deep Learning or Machine Learning?

1. Data Dependence

One of the most important differences between traditional machine learning and deep learning is how performance changes as the scale of data increases. When the amount of data is small, deep learning algorithms generally do not perform well, because they require large volumes of data to recognize and understand patterns effectively. Machine learning algorithms, however, can still work well in such situations.

2. Hardware Dependence

Deep learning algorithms depend heavily on high-end machines, because deep learning relies on GPUs, which are an indispensable part of its operation. Since deep learning performs large numbers of matrix multiplications, these operations can be optimized efficiently by using GPUs designed specifically for that purpose. In contrast, traditional machine learning algorithms can run on lower-end machines.

3. Feature Engineering

Feature engineering refers to the process of applying domain knowledge when creating feature extractors in order to reduce data complexity and make patterns visible to learning algorithms. This process is often expensive and difficult, requiring a great deal of time and expertise. In traditional machine learning, the features used by an application are identified by experts, who manually encode them according to the data type and domain. These features may include shape, pixel values, texture, orientation, and position. The performance of machine learning algorithms depends on the accuracy of the identified and extracted features. Deep learning algorithms, on the other hand, identify these high-level features directly from the data, reducing the need to develop a brand-new feature extractor for every problem.

4. Interpretability of Logic

Interpretability is also one of the factors that must be considered before applying deep learning in industry. For example, suppose we use deep learning to automatically grade an essay. Although it may perform extremely well, it does not reveal the reason behind the assigned score. You may be able to determine mathematically which nodes in the deep neural network were activated during scoring, but you still would not know what patterns those neurons represent or what they are doing together. This makes the result difficult to interpret. Machine learning algorithms, however, can often provide a clearer set of rules according to which a score was selected, making the underlying logic easier to explain.

Where Are Machine Learning and Deep Learning Used?

Computer Vision: useful for applications such as vehicle number recognition, facial recognition, and license plate recognition.

Information Retrieval: useful for applications such as search engines, image search, and text search.

Marketing: useful for applications such as target identification and automated email marketing.

Medical Diagnosis: useful for applications such as cancer detection and the detection of other serious diseases.

Natural Language Processing: useful for applications such as photo tagging, online advertising, and sentiment analysis.

Which One Should You Choose?

In this article, we discussed the differences between machine learning and deep learning technologies. Although deep learning is now better able to support many large-scale real-world applications that traditional machine learning cannot satisfy, machine learning still has strong value in many scenarios. Deep learning is a subset of machine learning, and compared with traditional machine learning algorithms, it has proven to be a more powerful and promising branch in the industry. Thanks to its layered structure, it has achieved many capabilities that traditional machine learning cannot. Self-driving cars, movie recommendation systems, and even advances in preventive healthcare are all results of deep learning, which has successfully made artificial intelligence part of both the present and the future.

However, although traditional machine learning cannot support some of these modern applications, it still has irreplaceable advantages in certain smaller-scale scenarios. Therefore, it is important to weigh the trade-offs carefully when choosing between the two in practice.