Artificial Intelligence Technology
Deep Learning Artificial Intelligence - Machine Learning TABLE OF CONTENTS What Is Deep Learning?
How Deep Learning Works Deep Learning vs. Machine Learning Special Considerations A Deep Learning Example What Is Deep Learning?
Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.
KEY TAKEAWAYS
Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions.
Deep learning AI is able to learn without human supervision, drawing from data that is both unstructured and unlabeled.
Deep learning, a form of machine learning, can be used to help detect fraud or money laundering, among other functions.
How Deep Learning Works
Deep learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world. This data, known simply as big data, is drawn from sources like social media, internet search engines, e-commerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible and can be shared through fintech applications like cloud computing.
However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information. Companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adapting to AI systems for automated support.
Deep learning unravels huge amounts of unstructured data that would normally take humans decades to understand and process.
Deep Learning vs. Machine Learning
One of the most common AI techniques used for processing big data is machine learning, a self-adaptive algorithm that gets increasingly better analysis and patterns with experience or with newly added data.
If a digital company its system, it could employ machine learning tools for this purpose. The computational algorithm built into a computer model will process all happening on the digital platform, find patterns in the data set, and point out any anomaly detected by the pattern.
Deep learning, a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a nonlinear approach.
Electronics maker Panasonic has been working with universities and research centers to develop deep learning technologies related to computer vision.1
that ensues, while a deep learning nonlinear technique would include time, geographic activity. network processes a raw data the next information its result.
Big Data .Deep Learning vs. Machine Learning.The next information network.unraveling this wealth of information and are increasingly adapting to AI systems for automated support.self-adaptive algorithm that gets increasingly better analysis and patterns with experience or with newly added data.However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information.
A Deep Learning Example
Using the fraud detection system mentioned above with machine learning, one can create a deep learning example. If the machine learning system created a model with parameters built around the number of dollars a user sends or receives, the deep-learning method can start building on the results offered by machine learning.
Each layer of its neural network builds on its previous layer with added data like a retailer, sender, user, social media event, credit score, IP address, and a host of other features that may take years to connect together if processed by a human being. Deep learning algorithms are trained to not just create patterns from all transactions, but also know when a pattern is signaling the need for a fraudulent investigation. The final layer relays a signal to an analyst who may freeze the user’s account until all pending investigations are finalized.
Deep learning is used across all industries for a number of different tasks. Commercial apps that use image recognition, open-source platforms with consumer recommendation apps, and medical research tools that explore the possibility of reusing drugs for new ailments are a few of the examples of deep learning incorporation.
unraveling this wealth of information and are increasingly adapting to AI systems for automated support.
Related Terms
Artificial Neural Network (ANN)
An artificial neural network (ANN) is the foundation of artificial intelligence (AI), solving problems that would be nearly impossible by humans. more
Machine Learning
Machine learning, a field of artificial intelligence (AI), is the idea that a computer program can adapt to new data independently of human action. more
Reading Into Predictive Modeling
Predictive modeling is the process of using known results to create, process, and validate a model that can be used to forecast future outcomes. more
Inside Data Science and Its Applications
Data science focuses on the collection and application of big data to provide meaningful information in industry, research, and life contexts. more
Neural Network Definition
Neural network is a series of algorithms that seek to identify relationships in a data set via a process that mimics how the human brain works.
AI Winter Definition
AI (Artificial Intelligence) winter is a time period in which funding for projects aimed at developing human-like machines intelligence
