Definition of Deep Learning and Its Examples in Businesses
You still don’t know the definition of deep learning? As the development of digital technology progresses, there are more and more technologies that can be used to run a business, one of which is deep learning. Now, many business sectors can be further developed by utilizing deep learning.
Through this article, you will learn more about what deep learning is, from what it means to how it works. Apart from that, you will also get several examples of implementing deep learning in business. Let’s read the full explanation below!
What is Deep Learning?
Deep learning is a machine learning method that is similar to how the human brain works. Deep learning uses an artificial neural network consisting of many connected layers (deep learning layers). These artificial neural networks can be programmed to recognize patterns and make decisions in a way similar to how the human brain works.
Deep learning is a subset of machine learning, which is a branch of computer science that focuses on building machines that learn automatically. Deep learning is one of the most effective methods for implementing machine learning because it can handle very complex data with a higher degree of accuracy.
Deep learning is often used in applications that require pattern recognition and data classification, such as facial recognition, voice recognition, and language translation. In addition, deep learning is also often used in recommendation systems, as we often encounter on music or movie streaming platforms, as well as big data processing.
How Does Deep Learning Work?
Deep learning uses artificial neural networks consisting of layers of interconnected neurons. Then, the layers of neurons will process data that enters the network. Each layer of neurons will receive data from the previous layer, process it, and send it to the next layer.
Deep learning works by introducing data into an artificial neural network, then modifying the weights contained in each neuron according to the pattern found in the data. Every time data is introduced into the network, neurons will update their weights to minimize errors that occur. Thus, artificial neural networks will continue to learn and become more accurate in predicting the desired results.
This process is repeated until the artificial neural network can produce accurate predictions with a tolerable error rate. After the training process is complete, the artificial neural network can be used to perform more specific tasks according to what has been learned during the training process.
7 Examples of Deep Learning in a Business
Deep learning is a part of computer science that is gaining popularity because it can be used for various business purposes. The following are some examples of implementing deep learning in a business.
1. Facial Recognition
Facial recognition using deep learning is one of the most popular applications. Deep learning can be used to identify a person’s face from a photo or video with a high degree of accuracy. This application is useful in security systems to identify people registered in a database or to authenticate access to a system.
To use deep learning for facial recognition, you first need a sizable data set consisting of photos of faces that have been labeled with a person’s name. The artificial neural network will then be trained using the data set to recognize patterns found on a person’s face. After the training process is complete, the artificial neural network can be used to identify a person’s face from a new photo or video.
2. Speech Recognition
Speech recognition using deep learning is a very useful application in the business field. Deep learning can be used to identify a person’s voice from an audio recording and convert it into text. This app is also useful in language translation or voice command recognition.
An example of implementing voice recognition with deep learning in business is a smartphone system that can understand voice commands. Users can use voice commands to access applications or control other electronic devices using only their voice.
In addition, speech recognition with deep learning can also be used in real-time language translation applications, so that people who speak different languages can easily communicate with each other. That way, meetings held with clients from abroad can be more helpful.
Recommendation systems using deep learning are applications that are widely used by large companies, especially those engaged in e-commerce or marketplaces and streaming services. Deep learning can be used to suggest products or services to customers based on their preferences identified through available data. This application is useful for increasing customer satisfaction and sales.
An example of implementing a recommendation system with deep learning in business is on a music or movie streaming platform. When a user watches a film, the recommendation system will suggest other films that the user might like based on the films he has watched before. In addition, recommendation systems with deep learning can also be used in marketplaces to suggest products to customers based on their purchase history.
4. Customer Care
Deep learning can be applied to customer care for a business by using a trained chatbot. This chatbot will help handle customer requests or questions more quickly and accurately. Deep learning itself will train the chatbot to be able to understand the patterns contained in conversations, so that the chatbot can provide appropriate responses.
In addition to using chatbots, deep learning can also be implemented in customer care ticketing systems to identify the same theme from multiple incoming reports and provide appropriate responses automatically. In addition, you can use deep learning in sentiment analysis systems to identify how customers are feeling from conversations that occur and provide appropriate responses.
5. Machine Maintenance
Deep learning is applied to implement predictive maintenance systems. The system uses deep learning to predict machine failure or damage by analyzing data generated by the machine during normal operation. If anomaly patterns indicating the possibility of failure or damage have been detected, the system will provide notification so that maintenance can be carried out before it is too late and prevent longer downtime.
6. Transaction Pattern Recognition
In finance, deep learning can be applied to analyze transaction patterns. Deep learning will analyze transaction patterns made by a person or a company and identify transactions that are unusual or not in accordance with the usual transaction patterns. This system is useful for preventing financial crimes such as money laundering or other illicit activities.
Deep learning can be applied in the field of marketing by analyzing data generated by consumers and determining appropriate marketing actions. Some marketing applications through the application of deep learning can be in the form of targeted advertising, email personalization, website optimization, and sales predictions.
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