What is Deep learning? Real-world examples and Challenges

What is deep learning, how does deep learning work, real-world examples of deep learning, challenges of deep learning

DEEP LEARNING, REAL-WORLD EXAMPLES AND CHALLENGES

What is Deep learning? In this blog we will see the basics of deep learning. Let’s think how Tesla always knows to stop at red lights or how Siri always recognizes your voice or how Netflix always manages to provide the most relevant recommendations? All of this is possible because of deep learning technology. But what do we even mean by deep learning technology?

Deep learning is a subset of machine learning. It is a learning technique that teaches machines problem-solving by example. Deep learning technology works by using massive databases, trained neural networks, and backpropagation algorithms to determine operations mimicking neurons in a human brain. These neural networks are capable of imitating the human brain without any human intervention.

Deep learning has evolved faster over the last few years. This sub-field is the advanced phase of machine learning. It is also important to understand the difference between machine learning and deep learning.

In machine learning, humans create algorithms that explore the data. Learn from it and derive analysis. Deep learning is radically different. The unique feature of this subfield is that it works on an Artificial Neural Network or ANN which bears a close similarity to the functioning of a human brain. ANN allows machines to analyze data in the same way that a human brain does. Machine with deep learning abilities does not need the support of human programmers they are stand-alone machines.

How does this work?

To make a deep learning machine that can differentiate between a soccer ball and a tennis ball when given visual clues. For this machine to work you need to design neural networks to distinguish between the two balls’ distinct features. This requires a process of teaching the machine to expect all variations in color, size, shape, etc. So it may learn to differentiate between the two objects. Once the machine is trained, it can learn on its own, then it will be able to distinguish between tennis and a soccer ball.

Real-World Examples of Deep Learning Technologies

Deep learning applications in Defense

Military systems implemented with Artificial Intelligence and Deep learning techniques can process larger volumes of data effectively. An infrastructure of this nature makes up a vital part of modern warfare due to greater computing and decision-making capabilities. In times of imminent threat, deep learning solutions systematize analysis. They also speed up the decision-making process. Deep learning simplifies intelligence gathering for better evaluation of on the ground as well as aerial battle scenarios. Understanding enemy behavior is another area in which deep learning can be applied.

More examples of the applications of deep learning in the military include target recognition, combat simulation, and training warfare platforms logistics, and transportation, cyber security, threat monitoring, etc.

Deep learning applications in Healthcare

Deep learning presents the healthcare sector with groundbreaking applications. The primary strength of deep learning lies in gathering a massive volume of data. In this sector, this data would include health records of patients, medical reports, advice of health care professionals, and insurance records. Deep learning will apply its advanced neural networks to come up with the best outcomes. Medical professionals and researchers can leverage deep learning to discover the hidden possibilities in data.

Some examples of applications of deep learning and healthcare include remote patient monitoring, genome sequencing, medical imaging, drug discovery, detection of medical insurance fraud, personalized treatment, etc.

Deep learning applications in Sales and Marketing

Deep learning can decode complex unstructured data. This attribute helps derive customer insights that are indispensable for creating a successful sales and marketing blueprint. Marketers can access insights from video analytics, images, facial recognition, speech recognition, and text analysis. They can analyze customer feedback and gauge their expectations on a real-time basis. Marketing today is mostly databased. Since deep learning can work with data at the scale every marketing campaign can be measured and evaluated with great accuracy. With deep learning on their side marketers can make more accurate forecasts. So they can achieve their sales campaigns and automate advertising tasks.

Deep learning applications in Manufacturing

State-of-the-art modern factories are fitted with manufacturing intelligence. This opens up a range of possible options for data access from sensors consisting of multiple structures, semantics, and formats. Deep learning promotes different levels of data analytics.

Descriptive analytics unravels historical data and evaluates operational parameters. Product condition at various stages of manufacture and environment to understand changes.

Predictive analytics estimates future production and equipment deterioration.

Diagnostic analytics records reasons for equipment failure.

Prescriptive analytics furnishes various scenarios to efficiently determine the course of action.

These up-to-the-minute real-time insights through deep learning drive high performance. Other advantages include lower operational costs, reduction and downtime, enhanced productivity, greater adjustment to customer needs, and higher value from operations.

Deep learning applications in Cyber security

Cyber security is one of the biggest real-world problems today. It concerns every large and small company throughout the world. Each day over a million new malicious software or malware threats are created. Sophisticated attacks are constantly paralyzing large corporations or even nations by targeting essential national infrastructures. Traditional cyber security solutions are struggling to detect new malware and counter it effectively. But deep learning addresses all the shortcomings of traditional cyber security solutions. The latest deep learning-based cyber security models are effective in attaining a higher detection rate and a lower false-positive rate for hidden malware when compared to traditional applications.

Self-driving cars like Tesla are based upon deep learning algorithms trained to be fully autonomous. These vehicles depend on their neural networks to navigate and safely drive on roads without any human intervention.

Translation software like Google translator and Microsoft translates employ deep learning technology to automatically recognize and translate your voice input into various languages.

Medical science – Deep learning technology has a significant aid in the field of medical science. For example, deep learning is used to detect disorders in the human genome structure. This massive computing power has given the exponential growth in scientific discovery and achievements.

Software applications like Netflix or Amazon, use complex algorithms from deep learning to tailor exactly the content or products or service requirements.

Why is deep learning meaningful?

Deep learning technology is of crucial importance to the future, in particular to the future development of other technologies. Having the ability to process extensive amounts of unstructured data with more power and accuracy than a human could ever accomplish, truly makes it one of the most important technologies of the future.

Who can benefit from this deep learning?

Everybody- engineers, medical professionals, law enforcement, government, scientists, students, children all can benefit from such powerful technology.

Challenges of Deep Learning

Data- There are large volumes of data that are required. For any deep learning machine to work efficiently and effectively. It requires an incredible amount of data so that most of our current systems would be overwhelmed by the massive inflow of this data.

Computational limitations to process massive data sets; this needs high-end GPUs and CPUs. Though most computer systems GPU’s and CPU’s are not able to handle this quantity of data, nor process it future systems certainly will. With the rise of more supercomputing and quantum computing, this challenge will likely be eliminated.

People- People need to trust these systems, their predictions, their recommendations. However, as we trust these systems the rate of adoption will happen at an exponential rate.

Algorithms used in Deep Learning

·       Convolution Neural Network (CNN)

·       Multilayer Perceptron’s (MLPs)

·       Deep Belief Networks (DBNs)

·       K-nearest neighbor method

·       Recurrent Neural Network (RNN)

·       Back Propagation

·       Self-Organizing Maps (SOMs)

·       Artificial Neural Network (ANN)