Introduction
You must have wondered how Google translates a whole paragraph into a different language in milliseconds. How is it possible to have self-driving vehicles? How is it that YouTube and Netflix can give personalized recommendations? These innovations result from deep learning. This article discusses deep learning, how it works, its applications, challenges, examples, and more.
Definition of Deep Learning
Deep learning reflects a subset of machine learning and artificial intelligence. Deep learning is machine learning that draws inspiration from the human brain structure. Its algorithms tend to analyze data within a logical system continuously. This helps deep learning construct patterns that relate to the human brain essential in making conclusions and decisions.
Deep Learning Vs. Neural Networks
Deep learning and Neural networks relate so closely that it can be challenging to differentiate them on the surface. Nevertheless, there is one significant aspect that you can distinguish them.
Deep learning involves extracting and transforming characteristics that tend to form a relationship between stimuli. This model can be used in various forms, including self-driving vehicles, computer games, speech recognition, etc. On the other hand, neural networks transmit data using neurons. Through multiple connections, they give output from the data transmitted as input. You can use neural networks to handle decision-making, clustering, pattern recognition, and more.
Differences Between Deep Learning, Artificial Intelligence, and Machine Learning
In recent years, technology has become quite advanced and complex. In as much as these developments are commendable, numerous terminologies have been used. Such words include Machine Learning, Artificial Intelligence, and Deep Learning. You might find these terms difficult or use them interchangeably. However, they are unique and different.
Artificial intelligence is the imitation of human behavior by intelligent machines. Meaning that the device has been trained to undertake a specific task in a particular manner. Machine learning is part of artificial intelligence. Machine learning helps computers handle situations through experience, observation, analysis, and training.
Note that machine learning is a part of artificial intelligence, but artificial intelligence is not a part of machine learning. Machine learning is one of the crucial tools you can use in your business operations.
DL counts as a subset of machine learning. Basically, it is defined as a deep artificial network. These networks are referred to as algorithm sets that accurately help in issues such as image recognition, sound recognition, and others. With DL, your computer can learn without programming it to do so.
Top 4 Reasons Why Your Organization Should Invest in Deep Learning
- Analyze unstructured data: Deep learning can be trained through varying data formats. Hence, it helps analyze unstructured data, which may be difficult for most machine learning algorithms.
- Data labeling: Data labeling might be an expensive and tedious task. With deep learning, you don’t have to ensure well-labeled data. Its algorithms operate accurately without needing guidelines.
- Efficiency: Deep learning can work on thousands of routine and duplicated jobs with proper training. This is done with accuracy, high quality, and in a short period.
- Training: Deep learning algorithms can learn on their own. The system can accurately verify its outputs or predictions and make necessary adjustments.
How Deep Learning Works
Deep learning networks learn by discovering complex structures in the information you feed them. During data processing, artificial neural networks classify the data. Usually, the process uses answers from several true or false questions that come in doubles. The network can represent data by creating multiple abstraction levels using sophisticated mathematical calculations.
For instance, let’s consider a facial recognition system. This program learns to recognize and detect the face’s lines and edges. Then move on to other significant parts of your face and the whole facial representation. As the program conducts some self-training, the chances of identifying the correct face enhances. In the end, the program can locate your face with high accuracy.
4 Key Applications Areas of Deep Learning
The main goal of deep learning is to benefit its users and society. Let’s explore some of its applications:
Law Enforcement
In recent years, deep learning is achieving groundbreaking outcomes in image processing, speech, and pattern recognition. Law enforcement agencies are now using these data analysis techniques. The use of fingerprint and face recognition significantly help in crime prevention.
Financial Services
Globally, insurance firms, asset management, venture capital companies, and banks face loan risks. This is because it can be challenging for your firm to assess the ability of a client to meet contractual obligations or repay a loan. The use of deep learning in these firms has helped in document processing. Hence, it is possible to assess a potential client’s credit risks.
Customer Services
Deep learning is a cost-effective solution that will help improve your operations and offer better customer services. Data obtained from this technology can help you make predictions on when a client requires assistance. Some aspects, like speech recognition programs, will help you know your customers’ needs and interests. Hence, you can provide services and products valuable to them.
Healthcare
Deep learning programs help interpret medical images such as CT scans, MRI scans, and X-rays. These are essential for diagnosing, flagging anomalies, and detecting risks in medical images. Commonly deep learning helps in cancer detection, drug discovery, and medical research.
Top 3 Challenges in Implementing Deep Learning
Deep learning is a good imitation of the human brain that can continuously learn and help solve a particular issue. Nevertheless, here are some significant challenges that you need to look out for:
Large Data Sets
Algorithms used in deep learning need large amounts of data to give accurate results. Just like a human brain requires much experience to deliver information, deep learning needs a lot of data.
Non-Transparent Systems
It can be challenging to understand how neural networks deduce conclusions. Hence, it is difficult to implement functions needing high-level cognition. Also, the non-transparent system does not support process verification, an essential aspect in some domains.
Lack of Flexibility
A deep learning system is incredible at solving a single issue. It is highly specialized in a specific problem. Hence, solving a closely related problem needs reassessment and retraining.
Deep Learning at Work Example
Assuming the aim is having a neural network identify a cat’s photo. You have to make several images incorporating different examples of cat faces labeled “cat.” Take a set of other object pictures that aren’t cats and label them “not a cat.” When input into neural networks, the images are transformed into data. The neural network learns from the image’s pixels and classifies them based on their features. In this case, those with eyes, four legs, claws, etc., deliver the output: a cat.
Conclusion
You now understand why deep learning is your best choice despite the few challenges. The system has much to offer that will help you enhance operations. Invest in deep learning today to achieve a tangible impact and to improve efficiency.
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