An overview of deep learning and its applications

Rohit Ayyagari is a software professional and independent researcher passionate about machine learning, deep learning and artificial intelligence.

His article, Dargan, S., Kumar, M., Ayyagari, M.R., and Kumar, G. (2019). An overview of deep learning and its applications: a new paradigm for machine learning. Archives of Computational Methods in Engineering, 1-22. provides insight into how deep learning can be used to save money and time across multiple industries such as healthcare, manufacturing, big data, and more.

Machine learning and Artificial intelligence have been at the forefront of technological breakthroughs over the past decade. The exponential advancement in the speed of computation over the past few years has allowed machine learning to become more accessible than ever before, allowing data science and, by extension, artificial intelligence to advance at an incredible rate.

The more computationally expensive cousin of machine learning

One of the main advantages of this technological improvement is the wider adoption of Deep learning technologies in industries and workplaces. Deep learning requires huge amounts of data and therefore orders of magnitude more computationally expensive than machine learning. The recent huge increase in computing power has allowed deep learning to be adopted more widely. To understand the significance of deep learning, we first need to understand machine learning.

Machine learning

Machine learning describes the use of statistical models to predict an outcome or solve a problem based on data entered into a model. What does it mean? This means that machine learning models use statistics and mathematics to predict an event, outcome, or solution to a specific problem based on a set of variables that the model dictates.

To simplify this even further, let’s say we would like to predict the salary of a new employee, we have data that contains salary, skills, years of experience, industry and other similar data that belong to 5,000 employees. The machine learning model will “train” on this data and figure out the patterns that lead to the fact that the employee receives a certain salary, and then build an equation based on this data.

For example, this equation might be that for an employee, his or her monthly salary is $ 1,000 plus years of experience multiplied by $ 600, which means that an employee with 10 years of experience would receive a salary of over $ 1,000. 10 x 600 = $ 7,000. This process, of course, radically simplifies the process itself, but it gives you an idea of ​​how machine learning models work. The model will use the given data (data from previous employees) to predict a specific outcome (salary of the new employee).

This process can be tailored using a variety of statistical methods and equations to suit specific needs or goals.

What about deep learning? And how is this different from machine learning?

Deep learning

“Deep learning is the most efficient, supervised, time-efficient and cost-effective approach to machine learning.”

In accordance with An overview of deep learning and its applications: a new paradigm for machine learning, one of the most cited research papers on the topic of deep learning applications, which can be found here.

Compared to machine learning, deep learning is much more flexible and can be applied to many more areas. It has also made several technological breakthroughs over the past 6 years, making it a superior option recently over machine learning in many areas.

To quote the document: “The widely used areas of deep learning are business, science and government, which also includes adaptive testing, biological image classification, computer vision, cancer detection, natural language processing, object detection, face recognition, handwriting recognition, speech recognition. , stock market analysis, smart city and much more. “

We’ll look at some of the more widely used applications later, but for now, how does deep learning work differently from machine learning?

How does deep learning work?

Deep learning uses artificial neural networks that are modeled after the neurons in the human brain, allowing neural networks to “learn” more than a human would learn. These neural networks are the main components of deep learning models, which is why deep learning models are often referred to as neural networks.

“Deep” in deep learning refers to the fact that these neural networks are stacked in multiple layers, like neurons in the brain, making these deep learning models “deep”.

According to the document, deep learning models operate in two stages: the learning phase, in which each “layer” of the model takes data as input, identifies patterns in the data and produces a result that the next level takes as input, identifies more patterns at a deeper level, and issues output, and so on until the data travels through the entire network.

The second stage is the inference stage, in which the model draws conclusions from the data it sees and assigns a label or result to the problem it is trying to solve.

Deep learning is more suited to solving complex problems than machine learning because it truly has the ability to “learn” from data. This nature also makes it easier to understand patterns in a deeper and more abstract way.

What difficult problems can deep learning solve?

Deep Learning Applications

Health care

One of the most important applications of deep learning is healthcare, where deep learning models are used to detect and diagnose various types of tumors, cancers and diseases. These models are sometimes as accurate as experienced doctors, but they are faster and in some cases more accurate.

The model can perform several hundred identifications per second, which is very fast. It has also allowed the development of telemedicine, which can provide health care to rural and less developed areas without physically seeing a doctor.

Self-propelled cars

Deep learning models can be used to create autonomous vehicles with data from multiple cameras that capture the environment and then inject it into a deep learning model with various separate models such as object detection and recognition to identify pedestrians and other vehicles, etc. This combination of models can allow the vehicle to be fully autonomous. We can expect to see this technology in the coming years.

Natural Language Processing (NLP)

Understanding natural human language is one of the most difficult tasks a machine can get used to. Context, semantics, tone, expressions, sarcasm, double meanings, and other vague parts of language can make it extremely difficult for a model to predict how a conversation is going and what a person might mean in a sentence.

Deep learning has made unexpected advances in NLP thanks to huge increases in computing power as well as more advanced techniques. This has allowed various fields to flourish, such as text and document generalization, question answering, text classification, sentiment analysis, speech recognition, word definition, and writer identification.

Conclusion

Deep learning It is expected to continue to make more and more strides thanks to the exponential increase in computing power, which will lead to the development of many areas and, in turn, become faster and more efficient. It is great to see how these new areas will affect human society and therefore the entire world.

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