These days various discussions about human tasks can be replaced by machines. While technology is advancing rapidly with fear and excitement, terms like artificial intelligence, machine learning, and deep learning may make you nervous.
Artificial intelligence simply means the performance of human tasks by intelligent machines. Deep learning is a subset of machine learning; which is capable of predicting outputs and creating decision patterns by artificial intelligence and imitating the functioning of the human brain. Deep learning refers to artificial neural networks that consist of several learning layers. It also uses many DNNs to learn the abstraction level.
Table of Contents
1. How do deep learning and neural networks work?
Deep learning has evolved in the digital age. which has caused the emergence of data called big data. This data is collected from social media websites, search engines, and e-commerce platforms. This big data is accessible and shared through supercomputers.
However, this big data is usually in an unstructured form. It may take years for humans to discover and extract relevant information from it. By relying on some artificial intelligence-based systems, companies understand the potential need for it and avoid wasting capital.
Currently, there are computers with sufficient capacity for deep learning models as well as big data for training deep learning neural networks. It is called deep learning because neural networks have different and deep layers that make learning possible. For almost any problem that needs to be thought about; Deep learning can be trained.
The performance of neural networks is getting better day by day; Because they are continuously fed and educated with more information. This is what differentiates deep learning from other machine learning techniques. In addition to increasing information, deep learning algorithms take advantage of the more powerful computing power available today. The development of artificial intelligence has also had a significant impact on this process. AI as a service has given smaller organizations access to AI technology, specifically the AI algorithms needed for deep learning.
2. Deep learning is a hierarchical “feature learning”.
What is feature learning?
Feature learning refers to a set of techniques that can learn a feature. For example, the classification of raw data. Feature learning works in the form of an AI hierarchy.
In addition to scalability, deep learning also allows us to learn features. In general, it makes learning complex processes easy for machines.
Deep learning helps exploit unknown structures in input data. In higher layers, deep learning features are divided into several layers. Learning features at multiple levels help machines understand complex deep learning systems.
3. Types of machine learning algorithms
Neural network algorithms in machine learning are broadly divided into four parts:
A: Supervised learning algorithm
Supervised learning algorithms try to model relationships and dependencies between desired predicted output and input features; To be able to predict output values for new data based on relationships learned from previous data sets. As a result, we need labeled data in the supervised learning algorithm to train the deep learning model. Labeled data contains target input and output.
B: Unsupervised learning algorithm
In this algorithm, computers are trained with unlabeled data. Unsupervised learning algorithms attempt to recognize patterns, summarize and group data by applying techniques to input data and help make meaningful recommendations. In this algorithm, we also need labeled data; But there is no target output.
C: Semi-supervised learning algorithm
This algorithm is between the two previous modes. In many situations, the cost of labeling data is high. Because it requires skilled professionals. Therefore, facing these cases, semi-supervised learning algorithms are the best choice for model building. This algorithm is based on the idea that although the unlabeled data set is uncertain; these data contain valuable information about group parameters.
D: Reinforcement learning algorithm
This algorithm is related to how to perform software actions in an environment. The reinforcement learning algorithm is trained for decision-making. This algorithm trains itself based on trial and error in decision-making.
4. Deep learning in machine learning
One of the most common artificial intelligence-based techniques for big data processing is machine learning. The self-adaptive algorithm continuously improves and educates itself based on patterns. Deep learning allows machines to solve complex problems even when using datasets that are highly diverse, unstructured, and interconnected. The deeper the learning algorithms learn, the better they will perform.
Let’s go with an example:
If a digital payment company wants to detect fraud in its payment system, it can use machine learning tools. The calculation algorithm built into the system can check all transactions. Therefore, according to different data sets, the pattern of anomalies occurring in the system can be observed.
These tools do the work automatically and prevent unauthorized access to the systems. Deep learning is a subset of machine learning that uses an artificial neural network (ANN) to perform processes.
The function of artificial neural networks is like small human brains, which are made with neural nodes connected in a network. Analysis in traditional programs is linear; However, the hierarchical nature of deep learning analyzes data using non-linear techniques.
A traditional approach to detecting fraudulent access to a digital system is based on transactions. The first layer of the deep neural network processes data such as the transaction volume and passes it on to the next layer. In the second layer, IPs assigned to users are checked and then forwarded to the next layer.
The next layer receives and processes the information obtained in the previous layer. In this layer, their geographical location is checked and then transferred to the next layer. In this method, deep learning examines patterns and identifies anomalies. When data reaches this hierarchy, it is better processed. They usually get more data sets to get better results.
Deep learning imitates humans and makes decisions through artificial neural network algorithms. Unstructured and unlabeled data can be processed with deep learning. Deep learning can also be used to detect money laundering in systems.
Deep learning patterns are not created just to capture trading patterns; They are also used to warn in case of fraudulent activities. The last layers alert an analyst; The analyst blocks the user’s account and stops all transactions.
Deep learning is used in all industries. For example, deep learning can be used in medical research as a tool to detect the possibility of reusing drugs. Google published an augmented reality based on machine learning in its research results.
Deep learning is also used in consumer and business applications that use image recognition. The main point is that deep learning neural network layers are not designed and hardwired by human engineers; Rather, they are created by the dataset and using the multi-objective learning process.