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Machine Learning vs. Deep Learning in Intelligent Systems

Updated: Feb 5

The author Julius Hoppenstaedt of the article:"Machine Learning vs. Deep Learning in Intelligent Systems"

Publication date: 27.09.2023

The realm of artificial intelligence (AI) is both fascinating and complex. Among its most talked-about subfields are Machine Learning (ML) and Deep Learning (DL). This article aims to dissect these terms. It focuses on their roles in modern intelligent systems, particularly in electronic markets and networked businesses.

The Evolution of Intelligent Systems

Intelligent systems transformation

Intelligent systems have undergone radical transformations over the past decades. They have evolved from early expert systems with handcrafted rules to today's advanced machine learning models. These developments were triggered by advances in programming, increased data availability, and more affordable computing power. For example, machine learning has automated the once tedious task of programming known relationships into systems. It now allows these systems to self-learn from data.

What is Machine Learning?

Machine learning is the backbone for many of today's intelligent systems. It automates the process of building analytical models, allowing systems to learn from data and improve over time. In business, machine learning finds applications in various sectors. It aids in employee retention in human resources. It also enhances customer relations through personalized experiences. Additionally, it plays a role in financial trading.

Deep Learning: A Specialized Subset

Deep learning is an advanced version of machine learning. It is rooted in artificial neural networks and can process unstructured data. These data types include images, texts, and sounds, which deep learning processes through multiple interconnected neurons. For instance, deep learning models excel in specific applications like complex game environments. In these settings, they often provide solutions that even surpass human capabilities.

Key Differences Between Machine Learning and Deep Learning

Machine learning has limitations with unstructured data. Often, it requires human intervention for feature engineering. In contrast, deep learning can process complex data types autonomously. To illustrate, think of machine learning as teaching a child about sportscars through various examples. Deep learning, however, would allow the child to understand the concept themselves, examining each feature in detail.

Implementing Intelligent Systems: The Challenges

Implementing Intelligent Systems

Both machine learning and deep learning have their challenges, especially in business settings. Some key issues include choosing the right implementation options. Others involve accounting for bias and drift in data. Additionally, there's the 'black-box' nature of deep learning models to consider. Developers and businesses must weigh these factors before implementing an AI system.

When To Choose What?

In summary, the choice between machine learning and deep learning depends on the problem you're facing. For structured data and less computational intensity, machine learning is often the better choice. For complex, unstructured data, deep learning might be more appropriate, although at a higher computational cost.

Understanding both machine learning and deep learning is crucial. Their capabilities and limitations play a significant role in modern intelligent systems. This is especially true in sectors that are becoming increasingly digitized, like electronic markets and networked businesses.


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