What is 'Deep Learning'

Deep Learning is an artificial intelligence 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 (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.

Also known as Deep Neural Learning or Deep Neural Network.

BREAKING DOWN 'Deep Learning'

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, online cinemas and more. 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 Artificial Intelligence (AI) systems for automated support.

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 new added data. If a digital payments company wanted to detect the occurrence of or potential for fraud in its system, it could employ machine learning tools for this purpose. The computational algorithm built into a computer model will process all transactions 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. A traditional approach to detecting fraud or money laundering might rely on the amount of transaction that ensues, while a deep learning nonlinear technique would include time, geographic location, IP address, type of retailer and any other feature that is likely to point to a fraudulent activity. The first layer of the neural network processes a raw data input like the amount of the transaction and passes it on to the next layer as output. The second layer processes the previous layer’s information by including additional information like the user's IP address and passes on its result. The next layer takes the second layer’s information and includes raw data like geographic location and makes the machine’s pattern even better. This continues across all levels of the neuron network.

Practical Applications of Deep Learning

Using the fraud detection system mentioned above with machine learning, we can create a deep learning example. If the machine learning system created a model with parameters built around the amount 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 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 to 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.

RELATED TERMS
  1. Neural Network

    Neural network is a series of algorithms that seek to identify ...
  2. Data Science

    Data science is a field of Big Data that seeks to provide meaningful ...
  3. Predictive Analytics

    Predictive analytics include the use of statistics and modeling ...
  4. Weak AI

    Weak AI is a machine intelligence that is limited to a particular ...
  5. Deep Market

    A deep market is a securities exchange where a large number of ...
  6. Knowledge Engineering

    Knowledge engineering is a field of artificial intelligence (AI) ...
Related Articles
  1. Investing

    Neural Networks: Forecasting Profits

    Take a look at the algorithmic approach to technical trading - you may never go back!
  2. Tech

    Cloud Battle Gets More Intelligent (MSFT, AMZN)

    It seems that machine learning technology is going to be the next battleground for market share within the cloud.
  3. Tech

    How Big Data and Artificial Intelligence Affect Investing

    Due to the increasing use of big data and artificial intelligence, investors are able to make more informed investment choices and grow their money.
  4. Tech

    5 Lessons From Google's Machine Learning Development (GOOG)

    Google challenges its employees to explore machine learning technology and strives to implement artificial intelligence across many of its products.
  5. Investing

    How Google, Facebook, Amazon Will Ride The AI Wave

    No Brainer: These 9 major stocks are poised to outperform over the next decade as they lead the AI revolution
  6. Investing

    Amazon, FB, Microsoft, Alphabet Form AI Non-Profit

    The giants of tech announced a non-profit partnership to collaborate on research and promote public understanding of artificial intelligence technology.
  7. Tech

    AMD Challenges Rivals, Aims for A.I. Market

    Continuing its long-standing rivalry with NVIDIA (NASDAQ: NVDA), Advanced Micro Devices (NASDAQ: AMD) earlier this month unveiled plans to bring to market chipsets that can be used as the foundation ...
  8. Investing

    Snap Poaches Top AI Engineer From Facebook

    Snap nabs an AI star from rival Facebook as the race to incorporate machine learning heats ups.
  9. Investing

    Google: AI Beats Hospitals at Patient Predictions

    Google claims that its artificial intelligence can better assess risks regarding patient outcomes.
  10. Investing

    NVIDIA to Gain on Role in Wal-Mart's Cloud Push

    Global Equities Research see WMT's use of NVIDIA's AI chips as 'incrementally positive.'
RELATED FAQS
  1. What are some common methods of gathering competitive intelligence (CI)?

    Read about some common methods of acquiring competitive business intelligence, and discover what a good intelligence analysis ... Read Answer >>
  2. What is human capital and how is it used?

    Learn about the concept of human capital, how it is developed and why it is important for businesses to protect their human ... Read Answer >>
Trading Center