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Fundamentals of Deep Learning

Deep Learning is a subdivision of machine learning that imitates the working of a human brain with the help of artificial neural networks.

Deep learning networks learn by discovering intricate structures in the data they experience. By building computational models that are composed of multiple processing layers, the networks can create multiple levels of abstraction to represent the data.

Deep learning systems require large amounts of data to return accurate results; accordingly, information is fed as huge data sets. When processing the data, artificial neural networks are able to classify data with the answers received from a series of binary true or false questions involving highly complex mathematical calculations.

Organizations use deep learning technology in many different ways, some not so expected. For example, deep learning can be used in translations. Although automatic machine translation isn’t new, deep learning is helping enhance automatic translation of text by using stacked networks of neural networks and allowing translations from images.

Deep learning can also be used in text generation. The machines learn the punctuation, grammar and style of a piece of text and can use the model it developed to automatically create entirely new text with the proper spelling, grammar and style of the example text. Everything from Shakespeare to Wikipedia entries have been created.

At its simplest, deep learning can be thought of as a way to automate predictive analytics. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.

Computer programs that use deep learning rely on algorithms in the hierarchy that apply a nonlinear transformation to its input and uses what it learns to create a statistical model as output.

Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is why deep learning is called “deep.”

Deep learning is especially beneficial for predicting outcomes whenever you have a lot of data to learn from. More specifically, a lot refers to a huge dataset with hundreds of thousands or better millions of data points. Where you have a huge volume of data like this, the system has what it needs to train itself.

Significant resources are being put into deep learning in financial services, in which it is used to detect fraud, reduce risk, automate trading and provide “robo-advice” to investors. According to a report from the Economist Intelligence Unit (EIU), 86% of financial services firms plan to increase their AI-related investments by 2025.

While all organizations can benefit from deep learning technology, some sectors are currently more in sync with deep learning than others. For example, cancer researchers are utilizing deep learning to detect cancer cells automatically.

In aerospace and defense, deep learning is also utilized to identify objects using satellites that locate areas of interest and identify unsafe or safe zones for troops.

Then there’s industrial automation where deep learning helps to improve the safety of workers around heavy machinery by automatically detecting when objects or people are within an unsafe distance from a machine.

Fundamentals of Deep Learning Course by Tonex

Fundamentals of Deep Learning is a 2-day training course address fundamentals of deep learning. This two-course online training program brings a hands-on approach to understanding the deep learning methods, algorithms, and computational tools used in modern engineering problem-solving.

Leveraging the rich experience of the faculty atDeep-learning-seminar Tonex, this program connects your deep learning, science and engineering skills to the principles of deep learning, machine learning and data science. With an emphasis on the application of deep learning methods, you will put these new skills into practice in real time.

 

Fundamentals of Deep Learning

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