What is Deep learning and how does it work?

Artificial Intelligence and machine learning stand as the foundation of the next breakaway in computing. In this article, you will learn how these technologies are used with Deep technology. You will also come to know the applications and examples of deep learning in real-time. Let’s begin with the proper definition of deep learning.

What is Deep Learning?

Deep learning can be defined as a subdivision of machine learning. It is a technique that teaches computers to function exactly as humans do. Deep learning is based on learning and uplifting on its own by analyzing computer algorithms.

Unlike Machine learning, deep learning uses simpler ideas and works on artificial neural networks. These artificial neural networks are drafted to mimic the idea of human thinking and learning. Moreover, deep learning has accelerated image classification, language transcription, speech recognition, pattern recognition, etc, all without human intervention.

Deep learning is composed of several layers of artificial neural networks. Each layer is capable of performing complex operations like representation and abstraction. Deep learning is one of the fastest surging fields in machine learning.

All in all, deep learning is an innovative representative of digital technology being used by a huge number of companies & newly-established business models.

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Types of Deep Learning

Now, you very well know what is deep learning. Let’s move on to the categorization of deep learning.

• Driverless Vehicles

Driverless vehicles can be categorized as a type of deep learning technique. Here, the car needs to react to the ever-changing environment around it to be safe on the road. The driverless vehicle comes under the course of deep learning algorithms.

The deep learning algorithm uses videos and images to position the vehicle in a better place so that it will be able to react to what’s happening in front of it. The algorithms comprise such data that makes a car act just like a human in terms of processing information and reacting in seconds.

• Virtual Assistants

The next type of machine learning is Virtual Assistant. Devices such as Cortana, Alexa depend on deep learning.

• Chatbots

Chatbots are yet another type of deep learning which helps customers more effectively and efficiently. Chatbots eliminate the need to hire a team of employees to talk with customers, and indisputably handle a queue of people having a query.

Applications of Deep Learning

Deep learning has innumerable applications; especially in the field of industrial automation technologies. Some of the major applications of deep learning are as follows:

• Defense Network: Deep learning is used in the defence system to identify objects and areas of interest. This ensures whether or not the area is safe for the troops to land.
• Medical research: Deep learning can instinctively identify the difference between cancer cells & healthy cells.
• Speech Automation: Today, deep learning is used to listen & study speech. This technology can generate more human-like speech patterns.
• Cybersecurity: Here comes one of the most crucial applications of deep learning; i.e. detecting cyber threats. Computers use deep learning to observe and perceive cybercrime based on the device’s behaviour within a network.
• Image recognition: Deep learning is now widely used to recognize the types of images presented to the system. Some of the examples are faces, vehicles, machinery, etc.
• Text generation: A deep learning algorithm can easily learn the grammar and punctuation rules of a language. It can create text that reads similar to human handwriting.

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Deep Learning examples

Deep learning models approach the information in the same way as our human brain does. Hence, they can be potentially applied to many tasks people do. Deep learning examples include the making of image recognition tools, natural language processing (NLP), and speech recognition software.

Deep learning use cases count in various types of big data analytics applications, particularly those engrossed with language translation, medical discoveries, stock market trading indicators, network security.

How does Deep Learning work?

Finally, let’s have a look at how deep learning works. Deep learning works based on neural networks. These networks are layers of nodes just like the neurons present in the human brain. Nodes are interconnected between the individual layers and adjacent layers.

The deeper the number of layers the deeper is the network. An artificial neural network receives signals that move between nodes and delegate corresponding weights. The final layer of the neural assembles the weighted inputs to yield output.

Deep learning systems obligate powerful hardware. It is primarily because this technology is made up of a large amount of data being processed and involves several complex mathematical calculations.

Precisely speaking, Deep learning systems work on large amounts of data for producing accurate results. It is due to the huge number of neural networks it works on that makes deep learning a more of a focused and sophisticated form of machine learning.