Deep Learning

Difference between machine learning and deep learning

Machine learning uses specifically prepared algorithms for data analysis purposes and then learns and makes decisions based on this analysis. Machine learning thus doesn’t use neural networks. While machine learning requires structured datasets, deep learning can draw on the artificial neural networks to effectively process even unstructured data and thereby independently teach itself.

Structure of neural networks

A neural network is an abstract, artificial replica of the human brain and consists of artificial neurons, which can be categorized into input, hidden, and output neurons. The input neurons receive information, the hidden neurons map internal information patterns, weight the inputted information according to these patterns, and forward the processed information to the output neurons, which consequently contain the ultimate decision. The more layers a neural network has, the more complex the matters that can be mapped.

The concept of deep learning

With deep learning, machines learn how to teach themselves as well as develop and improve themselves without human assistance. That can only be achieved by identifying and classifying patterns in existing data and information. When the obtained findings are correlated with other data, they become connected within a further context. The patterns learned by the machines enable them to independently make decisions based on these connections. The decisions themselves are constantly analyzed, which results in the information connections being assigned different weights: If a decision proves to be correct, it is assigned a higher weight. If it is amended, it is assigned a lower weight.

More and more layers containing hidden neurons and internal connections form between the input layer and output layer. The number of layers and connections determines what the actual output looks like.

Deep learning applications

Deep learning can be used wherever a large – and unspecific – data input can be searched for patterns and models and processed to produce a concrete result. Typical applications of neural networks include:

• Image recognition
• Speech recognition
• Optical character recognition
• Controlling complex processes (e.g. autonomous driving)
• Predictions for complex systems (e.g. customer behavior)
• Early warning systems
• Machine translation
• Economic models

In the marketing field, deep learning can be used to better understand customer needs, suggest suitable products or services to (potential) customers, or predict their buying behavior. These processes can also help identify ways to strengthen customer loyalty and retention over the long term.

Deep learning in the form of chatbots are also a popular choice in customer service for responding to customer questions. Modern algorithms make it possible to process increasingly complex inquiries. The more inquiries customers make, the greater the learning success of the chatbot.

In this respect, the evolution of emotion AI, in other words artificial emotional intelligence, is becoming more and more relevant for marketers. Although chatbots can now communicate with and help customers on a rational level, they still often lack a personal and emotional touch. Emotion AI, which is able to understand the cognitive and emotive side of human communication and sense intentions, can significantly optimize the customer relationship.