Deep learning can feel more like a buzzword for the world of artificial intelligence than a practical solution. Part of the reason for this outcome is a lack of overall knowledge about this concept.
This machine learning method uses input to predict output through the use of algorithms. This process is useful for virtually any task, but it is often used in financial circles because of the possibilities of predicting customer movements, stock prices, and more.
Humans generate gigabytes of data every day through decisions, actions, and behaviors. By turning these inputs into something usable, AI gives us opportunities to develop more solutions.
How Algorithms Start Learning with AI
Several of today’s algorithms and deep learning technologies use Tensorflow to create results, along with its interface with Python. This platform makes it easier to input multivariate calculus, linear algebra, and statistics probability in useful ways.
The algorithms used for deep learning and artificial intelligence use neural networks to discover the associations between the input and output layers. Between the data and the predictable solution sits a hidden layer (or multiple ones) that use the numerical representation of the information to correlate the desired computation.
The significant points of deep learning focus on the bias parameters and tunable weight. Without their mathematical presence in the algorithms, AI couldn’t learn as it does with this technology.
Once the neural network passes every input through the algorithm to the output layer, the deep learning process evaluates the accuracy of its prediction through loss function. Programmers can adjust the biases and weights of the deep learning structures to minimize the differences between realism and predictability through back-propagation to ensure each data point receives proper consideration.
Implications for the Finance and Banking Industries
The finance and banking industries collect a massive amount of data from their customers daily. Until the invention of deep learning and AI, there was not much value in this asset. Human evaluations could only process a limited amount of this information.
According to Deltec Bank, Bahamas-“This technology eliminates that barrier entirely. Not only can the industry examine current datasets to predict consumer needs, but it can also include legacy data that has not received an evaluation.”
That means the algorithms provided by this technology can produce individualization to any consumer, no matter how many people an institution serves.
Why Do We Call This Technology “Deep” Learning?
The reason why deep learning earns its name in the world of artificial intelligence is because of the multiple hidden layers this technology uses. Standard computing might offer a single evaluation area between the input and output layers to create predictable outcomes, while this technology uses a significant number of layers.
Then programmers add the biases and weights to each layer so that the AI has more computational power. It can examine the inputs, use the neural network to predict potential outcomes, and then offer an answer based on all of these structures.
Each “decision” that this technology makes improves its ability to approximate more complicated functions so that complex outcomes become possible.
Various neural networks currently exist so that businesses and industries can complete specific tasks. You might use a convolutional design for computer vision needs, while a recurrent structure would be more useful for NLP.
The implications of this technology are virtually unlimited because almost all of our next-level ideas and theories rely on the structures of artificial intelligence to create desired outcomes. Video game environments, self-driving cars, and image synthesis are all potential solutions. We might even reach a day when medical care, surgery, and creative roles like content writing become part of the AI world.
Disclaimer: The author of this text, Robin Trehan, has an Undergraduate degree in economics, Masters in international business and finance and MBA in electronic business. Trehan is Senior VP at Deltec International www.deltecbank.com. The views, thoughts, and opinions expressed in this text are solely the views of the author, and not necessarily reflecting the views of Deltec International Group, its subsidiaries and/or employees.
About Deltec Bank
Headquartered in The Bahamas, Deltec is an independent financial services group that delivers bespoke solutions to meet clients’ unique needs. The Deltec group of companies includes Deltec Bank & Trust Limited, Deltec Fund Services Limited, and Deltec Investment Advisers Limited, Deltec Securities Ltd. and Long Cay Captive Management.