Deep learning is a field of machine learning that uses programming languages like Python to teach computers to experience the learning process as humans do: that is, learning by example. Machine learning is a very broad field whose history goes back a long time. Originally, this science was known as pattern recognition, but over time, the algorithms, mathematically, became much broader and of course more complex. In machine learning, two concepts of neural networks (inspired by the structure of the brain) and deep learning are used. Deep learning algorithms have a special architecture with many layers flowing in a network. Therefore, deep learning is part of machine learning and machine learning itself is part of artificial intelligence.

Behind such activities as driverless cars is a key technology called deep learning, which gives the car the power to recognize a pedestrian from a car’s taillight. Also, this technology enables the voice assistant to perform optimally on tablets, mobile phones, and televisions. Deep learning has attracted a lot of attention in recent years; because he has achieved successes that were not possible before.

In deep learning, a computer model learns to perform specified tasks through audio, images, and text.

While deep learning was theorized for the first time in the 1980s, its application and usefulness have recently been identified for 2 reasons:

1) Deep learning requires a huge amount of labeled data. The driverless cars of our example required millions of photos and thousands of hours of video.

2) Deep learning requires significant computing capability. High-performance GPUs have a balanced architecture that enables deep learning. This feature allows the development team to reduce the training time for the deep learning network from weeks to hours when computing data in a clustered and mass manner.

Concrete examples of deep learning

The field of automatic driving

Experts in the field of automated driving use deep learning to recognize objects such as stop signs, no-entry, and traffic lights at intersections. In addition, deep learning gives the automatic driving system the ability to recognize pedestrians, which ultimately leads to a significant reduction in accidents. This feature is also very popular with insurance companies.

Aerospace field

Deep learning identifies and analyzes the data it receives through satellites about the target area and identifies safe and unsafe areas for internal forces.

The field of industrial automation: Deep learning enables factories to keep their workers safe. In such a way that heavy and dangerous machines automatically detect whether people or objects are placed at an unsafe distance or not.

Electronics field

Deep learning has been used more in this field so far and we may deal with it in our daily life. For example, the voice assistant hears the voice analyzes it, and gives an appropriate response. A home appliance voice assistant that not only recognizes your voice but also knows your preferences.

Field of visual arts processing

The use of image processing in visual arts is increasing day by day. For example, in identifying the period to which a painting belongs, specifying the style of artwork and performing it on an image or video can be done by deep learning models.

How does deep learning work?

Most deep learning models use artificial neural networks, that’s why these models are also called deep neural networks.

The term “deep” refers to the number of hidden layers in the neural network. Neural networks could have 2 or 3 layers, but today deep neural networks can have as many as 150 layers.

Deep learning models require a large amount of labeled data and neural network architecture. These models extract features automatically and do not require manual feature extraction.

One of the common models of deep neural networks is the convolutional neural network, which is called CNN or ConvNet for short.

What is the difference between machine learning and deep learning?

As we read in the beginning, deep learning is a special mode of machine learning. Machine learning starts with manually extracting features. These features are used to build a model that performs the task of classification. But in deep learning, the feature extraction process is not manual and is done automatically.

Another difference is that in deep learning, the model expands as the number of data increases, but this is not the case in machine learning, and the model expands to a certain extent, and after that, it does not change with the increase of data. One of the key features of deep learning is that it improves with increasing data.

Machine learning or deep learning?

Machine learning offers a set of techniques and models that you use based on the application, the size of the data you are processing, and the type of problem you are facing. A successful deep learning model requires a large amount of data to train the model and GPUs (graphics processors) to process your data quickly.

When you have to choose between machine learning and deep learning, consider the availability of GPUs and the amount of data you have. If you don’t have access to a lot of data and GPU, your choice can be machine learning. Deep learning is a complex process, you need a lot of data, and to process this data, you need a graphics processor to process this huge amount of data quickly.

What capabilities does deep learning have over other programs?

Writing a program is a very difficult task. In the past, computers were so slow and memory was so expensive that they had to turn to the science of logic; this is the science on which current computers work. Logic is the basic language of machines by which information is processed bit by bit. But computers were still too slow and calculations too expensive.

But now the cost of calculations is decreasing day by day, and on the other hand, the cost of labor is constantly increasing. The cost of computing has become so cheap that getting a computer to learn a program is cheaper than hiring a person to write the same program. At this point, deep learning began to solve problems that no human had ever written a program for; Areas such as computer vision and translation were examples of this development.

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