Main menu


What is deep learning technologyDeep learning: It is a machine learning method that prepares computers to perform various tasks and functions naturally with humans. Deep learning is a key technology in driverless cars, which enables them to recognize stop signs and can distinguish a pedestrian from a lamppost, or as it happens in a voice control switch on user devices such as smartphones, tablets, TVs, and speakers without the need to use hands.

Artificial intelligence (Al) is what gives machines the ability to learn from experiences and adapt to new data, as well as simulate human behavior in many different tasks.

Most of the examples of artificial intelligence you hear about today, from self-driving cars to AI robots, rely heavily on deep learning and natural language processing; Computers can be trained to perform specific tasks by processing large amounts of data and recognizing patterns.

In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound, and deep learning models can achieve sophisticated accuracy that sometimes exceeds performance at the human level, and models are trained using a large set of labeled data and neural network architectures that contain on multiple layers.

What is deep learning technology

How does deep learning work?

Computer programs that use deep learning go through the same process. Each algorithm in the hierarchy applies a nonlinear transformation to its inputs and uses what it learns to generate a statistical model as an output. Iterations continue until the outputs reach a good and acceptable level of accuracy. The number of processing layers that the data passes through is what inspired the deep layer.

Because this process mimics a system of human neurons, this form of learning is sometimes referred to as deep neural learning or deep neural networks, and unlike a child who will take a few weeks or even months to understand the concept of “cat,” for example; A computer program that uses said learning algorithms can, through training, show and sort millions of images and the exact ability to identify those images where there are cats in a few minutes.

To achieve an acceptable level of accuracy, deep learning programs require access to vast amounts of training data and processing power, neither of which was readily available to programmers until the age of big data and cloud computing. Because deep learning programming is able to create complex statistical models directly from its own iterative output, it is able to create accurate predictive models from large amounts of unstructured data. Machines are not regulated and not categorized.

Its use today includes all kinds of big data analytics applications, especially those focused on Natural Language Processing (NLP), language translation, medical diagnostics, stock market trading signals, network security, and image capture.

Examples of deep learning applications

Since it processes information in ways similar to the human brain, these models can be applied to many tasks that people do. Deep learning is currently used in most common image recognition tools, processing protocols (NLP), and speech recognition programs, and these tools are starting They appear in various diverse applications such as self-driving cars, language translation services, and various applications such as Facebook.

The limits of deep learning

The biggest limitation to this learning process is that it takes place through observations, meaning that the information is only limited to what was in the data on which the models were trained, i.e. if the user has a small amount of data or a specific source; This does not necessarily represent the broader functional area, and the models will not learn in a way that can be generalized.

The issue of skewness is a major problem for deep learning models. The model will reproduce these deviations in its predictions, and this has been an annoying problem for deep learning programmers because models learn to differentiate based on subtle differences in data elements, meaning for example that a face recognition model may make decisions about people's characteristics based on things like race or Gender without the programmer being aware of it.