Deep Learning: Definition, Benefits, and Challenges

The concept of deep learning raises numerous questions for those unfamiliar with the technology in practice. What exactly is deep learning, what are its technical foundations, and how can it benefit tech companies? Let's explore the fundamentals.

As a branch of artificial intelligence (AI), deep learning drives numerous innovations, such as self-driving cars and voice and image recognition. This technology has permeated many aspects of human life. In 2023,?the global market for deep learning will be almost US$70 billion and will exceed US$1185 billion by 2033 . They predict an unprecedented CAGR of 32.57% in the next decade.

The huge interest in both machine and deep learning technologies is based on their advantages. So, what is deep learning? Let’s clarify this.

In summary, deep learning solutions employ a hierarchical approach to identify and compare the most significant features.

The Definition of?Deep Learning

Deep learning is a set of machine learning algorithms that represent high-level abstractions in data using architectures made up of multiple nonlinear transformations. But what does this mean?

Deep learning technology is founded on artificial neural networks (ANNs). These ANNs constantly receive learning algorithms and increasingly large datasets to improve the efficiency of their training processes. The larger the data volumes, the more efficient the process becomes. The training process is termed "deep" because, over time, the neural network encompasses more levels. The "deeper" the network goes, the higher its productivity.

How Deep Learning Works

A?deep machine learning process consists of?two main phases: training and inferring. The training phase involves labeling large amounts of data and determining their matching characteristics. The system then compares these characteristics and memorizes them to make correct conclusions when it encounters similar data again.

A?deep learning training process includes the following stages:

1. ANNs ask a?set of?binary false/true questions.
2. Extracting numerical values from data blocks.
3. Classifying data according to?the answers received.
4. Labeling Data.

During the inferring phase, the deep learning?AI draws conclusions and labels new unexposed data using its previous knowledge.

What Is?the Difference between Deep Learning and Machine Learning?

Deep learning is a subset of traditional machine learning. Classical machine learning involves extracting new knowledge from a large dataset loaded into the machine. Users define the training rules and correct any errors made by the machine. This approach helps avoid the negative overtraining effect of deep learning.

In?machine learning, users provide a?machine with both examples and training data to?help the system make correct decisions. This principle is?called supervised learning. In other words, in classical machine learning, a computer solves many tasks but cannot form such tasks without human control.

Diversity between machine learning (ML) and deep learning (DL):

• DL?requires a?lot of?unlabeled training data to?make concise conclusions, while ML can use small amounts of data provided by?users.
• Unlike ML, DL?needs high-performance hardware.
• ML?requires features to?be?accurately identified by?users, while?DL creates new features by?itself.
• ML?divides tasks into small pieces and then combines the received results into one conclusion, while DL solves the problem on an end-to-end basis.
• In?comparison with ML, DL?needs much more time to?train.
• Unlike DL, ML?can provide enough transparency for its decisions.

The concept of?deep learning implies that the machine creates its functionality by?itself as?long as?it?is?possible at?the current time. To?infer, deep learning applications use a?hierarchical approach involving determining the most important characteristics to?compare.

Advantages of?Deep Learning

In 2016, Grand View Research (GVR)?estimated ?the global deep learning market at $272 million. Its significant part (20%) belonged to?both the aerospace and defense industries. Since 2014, the deep learning market has shown continuous parabolic growth. GVR’s latest?report ?states that this market will reach a value of $10.2 billion by the end of 2025. So, what caused such?remarkable market growth? The answer lies in?the set of?advantages provided by?deep learning technology.

Creating New Features

One key advantage of deep learning over other machine learning algorithms is its ability to generate new features from a limited set of features in the training dataset. This means deep learning algorithms can create new solutions to address current problems. For data scientists working in technological startups, this capability allows them to tackle complex tasks more efficiently and innovate rapidly.

Since deep learning can create features without human intervention, data scientists can save a lot of time working with big data by relying on this technology. It also allows them to use more complex sets of features compared with traditional machine learning software.

Advanced Analysis

One key advantage of deep learning over other machine learning algorithms is its ability to generate new features from a limited set of features in the training dataset. This means deep learning algorithms can create new solutions to address current problems. For data scientists working in technological startups, this capability allows them to tackle complex tasks more efficiently and innovate rapidly.

Deep Learning Challenges

Deep learning is an approach that aims to mimic human abstract thinking (or at least attempts to approximate it) rather than directly using it. However, despite its many benefits, this technology also has several significant disadvantages.

Deep learning is an approach that seeks to emulate human abstract thinking (or at least attempts to approximate it) rather than directly employing it. Despite its numerous benefits, this technology comes with significant disadvantages. Let's explore some of the challenges associated with deep learning.

Continuous Input Data Management

In?deep learning, a?training process is?based on?analyzing large amounts of?data. However, fast-moving and streaming input data provides little time to ensure an?efficient training process. That is?why data scientists have to?adapt their deep learning algorithms in?the way neural networks can handle large amounts of?continuous input data.

Ensuring Conclusion Transparency

Another important disadvantage of?deep learning software is?that it?is?incapable of?providing arguments for its conclusions. Unlike in traditional machine learning, you cannot follow an algorithm to determine why your system has decided that a cat in a picture is a cat, not a?dog. You must revise the whole algorithm to correct errors in DL algorithms.

Resource-Demanding Technology

Deep learning is?a?quite resource-demanding technology. It?requires more powerful GPUs, high-performance graphics processing units, large amounts of?storage to?train the models, and more time to train than traditional machine learning.

Despite all its challenges, deep learning discovers new, improved methods of unstructured big data analytics for those who intend to use it. Indeed, businesses can gain significant benefits from using deep learning in their data processing tasks. However, the question is not whether this technology is useful but rather how companies can implement it?in?their projects to?improve the way they process data.

Let’s check the most common questions that arise in the context of deep learning.

FAQ

What is Deep Learning?

Deep learning is a type of machine learning that uses deep neural networks (DNNs), which are multilayered networks that simulate the workings of the human brain. These neural networks are trained to make decisions based on large datasets. Deep learning algorithms can recognize and classify phenomena, detect patterns and relationships, assess possibilities, and make estimations, predictions, and decisions.

So, how does deep learning work? Information enters through the input layer, is transported and processed through multiple layers, and finally, a prediction is made, or a result is outputted on the output layer. Deep learning powers much of today's AI, driving various applications and services that enhance automation by performing analytical and physical tasks without human intervention.

What is an example of deep learning?

Deep learning can address a wide range of problems. It recognizes images and speech, processes natural language, and more. Consequently, it's widely used in digital assistants, voice-enabled devices, and remote controls. It aids in detecting fraudulent actions, powers self-driving cars, and enables generative AI. In customer service and healthcare, deep learning is used in chatbots, virtual assistants, call centers, and other tools. It also supports law enforcement by quickly and accurately analyzing and learning from transactional data (such as sound and video records, documents, and images) to detect dangerous, fraudulent, or criminal activities. Predictive analytics, widely used in financial services, assesses business risks, trades stocks, detects fraud, and manages investment or credit portfolios.

What are the three types of deep learning? Now that we understand deep learning let's explore its three types: supervised, unsupervised, and reinforcement learning. In supervised learning, the machine is "supervised" by a human operator who feeds the algorithm data to help it learn. In unsupervised learning, the machine autonomously detects less obvious patterns in the data. Like human learning, reinforcement learning involves algorithm learning by interacting with the environment and receiving positive or negative rewards.

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Originally published at Stfalcon.com .

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