What is Deep Learning?
Deep learning is an artificial intelligence function that mimics the functioning of the human brain in data processing and the creation of models for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning without supervision from unstructured or unlabeled data. Also known as deep neural learning or deep neural network.
How Deep Learning Works
Deep learning has evolved in tandem with the digital age, which has caused an explosion of data in all its forms and in all regions of the world. This data, simply called big data, is extracted from sources such as social media, Internet search engines, e-commerce platforms and online cinemas, among others. This huge amount of data is easily accessible and can be shared via fintech applications like cloud computing.
However, the data, which is not normally structured, is so large that it could take decades for humans to understand it and extract relevant information. Businesses are realizing the incredible potential that can arise from exploring this wealth of information and are increasingly adapting to AI systems for automated support.
Deep learning learns from vast amounts of unstructured data that humans could normally take decades to understand and process.
Deep learning versus machine learning
Machine learning is one of the most commonly used AI techniques for processing big data, a self-adapting algorithm that gets better analysis and patterns with experience or with newly added data.
If a digital payment company wanted to detect the occurrence or potential of fraud in its system, it could use machine learning tools for this purpose. The calculation algorithm built into a computer model will process all transactions occurring on the digital platform, find models in the data set and report any anomalies detected by the model.
Deep learning, a subset of machine learning, uses a hierarchical level of artificial neural networks to carry out the machine learning process. Artificial neural networks are built like the human brain, with neural nodes linked together like a web. While traditional programs build data analysis in a linear fashion, the hierarchical function of deep learning systems allows machines to process data with a non-linear approach.
A traditional approach to detect fraud or money laundering might depend on the amount of transactions that ensues, while a non-linear deep learning technique would include time, geographic location, IP address, the type of retailer and any other characteristic likely to point to fraudulent activity. . The first layer of the neural network processes a raw data input as the amount of the transaction and transmits it to the next layer as output. The second layer processes the information from the previous layer by including additional information such as the user’s IP address and transmits its result.
The next layer takes the information from the second layer and includes raw data such as geographic location and further improves the machine model. This continues at all levels of the neural network.
Key points to remember
- Deep learning is a function of AI that mimics the functioning of the human brain in processing data for use in decision making.
- Deep AI allows learning from both unstructured and untagged data.
- Deep Learning, a machine learning subset, can be used to detect fraud or money laundering.
An example of deep learning
Using the fraud detection system mentioned above with machine learning, an example of deep learning can be created. If the machine learning system has created a model with parameters built around the number of dollars a user sends or receives, the deep learning method can begin to build on the results offered by machine learning. .
Each layer of its neural network builds on its previous layer with added data like a retailer, a sender, a user, a social media event, a credit score, an IP address and a host of other features. which can take years to connect if treated by a human being. Deep learning algorithms are trained to not only create models from all transactions, but also to know when a model signals the need for a fraudulent investigation. The final layer sends a signal to an analyst who can freeze the user account until all pending investigations are completed.
Deep learning is used in all industries for a number of different tasks. Commercial applications that use image recognition, open source platforms with consumer recommendation applications and medical research tools that explore the possibility of reusing drugs for new ailments are just a few examples deep integration.
Electronics maker Panasonic has worked with universities and research centers to develop deep learning technologies related to computer vision.