Deep learning is the sub-branch of machine learning, which in turn is the sub-branch of artificial intelligence. It trains the artificial neural networks to learn the data automatically from the previous experience and improve the quality of the content every time with the diverse experience. It is the most trending topic in the technical world; therefore, we will discuss it from scratch.
Today, we will learn the basic concepts of deep learning and how the technical world is rapidly changing because of the innovations in deep learning. We will discuss the introduction and history of deep learning and how it has evolved with time. After that, we will discuss some important fields where the technical departments are getting more efficient results by integrating the techniques of deep learning into their work. We will discuss different departments that use deep learning to improve the user experience and create new techniques with the help of user behavior analysis. So let’s start with the introduction of deep learning.
What is Deep Learning?
Deep learning is a complex network of artificial neural networks. Just like the human brain learns from the network of neurons, deep learning trains, learns, and understands through the vast interconnected network. This network has three types of layers:
1. Input layer
2. Hidden layer
3. Output layer
The input layer is the entry point of the data and in the hidden layers, the learning process is carried out. Once the calculations are performed on the data, the result is sent to the output layer. In interconnected networks, the output layer is connected to the input layer of the next deep learning network. The complexity and performance of the deep learning network depend on the number of hidden layers in it. As a result, these networks work on the classification of the data to predict the results by comparing and studying its behavior in different ways. The neurons in the neural network are arranged according to different weights and biases, and this is the basic structure through which the network learns from the calculations.
Evolution in Deep Learning
The work on deep learning started in the 1950s when artificial neurons were trained. It gave the confidence to work on deep learning because it was helpful in pattern recognition. After that, the work on this technique was stopped because of the technical limitations. In 1986, the backpropagation method was developed on the given data, and this opened new doors for the researchers to work more on deep learning. After that, many researchers were attracted to this technique because of the automation in the learning.
At that time, dealing with deep learning techniques was still challenging because of the limited research and the complex work with the computational algorithms. People were working with a single hidden layer, and the results were not satisfactory. Still, this was considered an important technique.
In the 2000s, powerful computers and larger datasets allowed researchers to work on these techniques at a faster rate, and it was the point where people were attracted toward deep learning. The convolutional neural network CNN gained fame in image recognition, and this was one of the most important steps toward the success of deep learning. Similarly, recurrent neural networks were useful in speech recognition and related tasks.
From the 2010s to the present, multiple techniques have been used in deep learning that are incorporated into different fields of science and technology. These are improving at a rapid rate and it is estimated that in the future, all businesses, technical fields, and even daily life will be improved with the help of deep learning techniques and algorithms.
Forward Propagation in Deep Learning
In the layers of deep learning networks, the nodes are the basic element that creates the connection between the networks. Each node takes the data from the previous one to perform more calculations and provide a more refined prediction and categorization of the data. The continuous progression of the data from one node to the next is called forward propagation.
Back Propagation in Deep Learning
Backpropagation in deep learning is an algorithm for extracting the required results from the data. In this, the errors are calculated on the output layer by comparing them with the real data. As a result, it propagates the error through the layer back and performs the required measure to get better results. It is an important algorithm in neural networks because it allows the network to perform complex tasks such as image recognition, natural language processing, machine translation, voice recognition, etc.
The illustration given above is the most basic and simple way to understand deep learning processes. Together, forward propagation and backpropagation allow the network to learn better at a faster rate. It is a complex process and requires the knowledge of multiple concepts if the reader wants to know all the basics and mechanisms of working deep learning networks. Overall, deep learning is a powerful network that is designed to learn from the data and classify it into different groups.
How Deep Learning is Redesigning the Technical World?
This is the era of technology, and we can see that the devices, methods, and technologies, all are changing at a rapid rate. For instance, communication devices are now able to provide better security and safety with the help of face locks and voice passwords. There are multiple examples of revolutions in technology, and most of these are because of deep learning and related fields. These techniques can be incorporated into almost every device to make it better. The combination of different fields with deep learning results in the working of that particular field because deep learning makes the process more automatic and provides the best performance. Here are some major examples where deep learning is playing a vital.
The data analysis is now better with the incorporation of deep learning because, in deep learning, the data is classified into groups and each and every part of the data is analyzed in detail. This helps to study the points that were hidden before, and as a result, better output is obtained. It helps a lot in fields like finance, marketing, health, etc. where more details of the provided data result in a better understanding of the scenario.
Natural Language Processing
NLP is the branch of computer science dealing with computer-human interaction, and it depends on deep learning processes because it has to understand the patterns of the language. It allows the computer to talk like a human and deep learning helps produce products such as human chatbots, sentiment analysis, translation services, etc. This makes the human-computer interaction better with more details and realistic chats.
Deep learning allows the computer to act like humans, and it is mostly used in fields like content creation, whether it is voice, text, or images. It can create realistic content based on the user’s requirements. Before this, content creation was a difficult task, and a lot of effort and time were required for the creation of quality content. Deep learning is improving the content industry, and it can create human-like content without errors in just a few seconds. Multiple tools on the market are now using deep learning mechanisms to create and provide the best information in the form of content.
Autonomous System with Deep Learning
The automobile industry is using deep learning to improve its products. This helps a lot with the automation of the most basic and important tasks. Deep learning provides the basis for automatic vehicles such as self-driven cars, robots, drones, etc.
These are sensitive applications that require a great deal of care and calculations. Using deep learning on simple devices makes them more accurate than humans. Such applications rely on neural networks to collect information and perceive the environment to make the right decisions in the shortest time. As a result, deep learning provides the methods to turn imagination into creation. It not only provides accuracy but also allows operating in hazardous places where human interaction is not possible.
Banking and Finance
Sensitive fields like banking and finance are getting help from deep learning to study information from different resources. This helps them have a secure and fraud-free system. Deep learning algorithms are able to detect suspicious information in different ways. The sentiment analysis and customer behavior analysis help the system to warn against unauthorized use. Moreover, it also detects issues in the data in the form of text and images. Therefore, it helps to deal with credit card and insurance fraud easily.
Customers are more comfortable working with banking systems using automatic recognition and techniques like deep learning to provide better financial safety. Moreover, it allows the bank to get more benefits from the same resources and, as a result, save on the costs of work.
Medical Industry with Deep Learning
The medical industry is using deep learning techniques to provide better health facilities and results. It depends on the sensitive information, which is normally not understandable by humans. Usually, these pieces of information are expected to be checked by experts only, but deep learning is a helping hand for such types of analyses. However, this technology is in its developing phase, but it helps to recognize diseases and suggest possible remedies according to the data inserted in the network.
With the advancement of technology, deep learning is able to detect the disease in its early stages, which is lifesaving in most cases. Moreover, this is also used in medical image analysis, drug discovery, personalized treatments, etc. This is a big relief for the patients and doctors as well.
Deep Learning in Businesses
The more automation in a business by Deep Learning, the better the outputs. Deep learning is extensively used in business and related fields for basic tasks. The creativity of the business can be enhanced with the help of deep learning technology. There are multiple ways to use deep learning to grow the business such as:
- The chatbots and automatic responses to the customer’s message help the business provide instant communication with the customers. These automatic messages have the ability to chat, share information, resolve issues, and even provide appointment dates.
- The marketing and sales departments of the business are using deep learning for better engagement with their businesses. Applications such as Facebook, YouTube, etc. show the relevant data to the users to provide a better user experience. These applications use deep learning algorithms to engage more people in their apps.
- The operational information of the business can be handled with deep learning algorithms for better performance. It can learn from the user’s responses and by improvising from these responses, design new strategies according to the trends.
Scientific Research with Deep Learning
Deep learning is an accelerating force in research related to science and technology. It can create models that can analyze large data sets in a minimum amount of time and can create efficient and important research without errors. It helps to work with complex simulations and create useful predictions from them. It is a big relief for fields like genomics and particle physics where accuracy and complex calculations cause slow research with more chances of errors.
Hence, deep learning is a technique to learn, predict, and classify large data sets with the help of artificial neural networks. There are multiple types of deep learning neural networks, such as traditional and modern neural networks. These networks are chosen on the basis of the type of research and data sets. At the start, this was a difficult technique to analyze the data, but with the advancement in machinery and technique, deep learning resulted in better performance and efficiency.
Web Development with Deep Learning
Deep learning can help programmers in various ways, enhancing their capabilities and streamlining their work in several aspects of software development. Here are some ways in which deep learning can be beneficial to developers:
- Automation of Repetitive Tasks: Deep learning models can be trained to automate repetitive coding tasks. For example, they can assist in generating boilerplate code, writing unit tests, or even auto-generating code snippets, which can save programmers time and reduce the risk of human error.
- Code Completion and Suggestions: Deep learning models can provide intelligent code completion and suggestions as programmers type, offering context-aware recommendations for function names, variable names, and code structures. This improves coding efficiency and helps catch errors early in the development process.
- Bug Detection and Code Quality Analysis: Deep learning can be used to build tools that automatically identify bugs, code smells, and potential security vulnerabilities in code. This can lead to improved code quality and reduced debugging time.
- Optimizing Algorithm Design: Deep learning can help programmers optimize their algorithms by providing insights into which approaches are likely to perform best for a given problem. It can help with algorithm selection and hyperparameter tuning.
- Documentation Generation: Deep learning models can help in generating documentation for code, explaining how functions and classes work, which can improve code readability and collaboration among developers.
- Code Translation: Deep learning-based translation models can assist programmers in translating code from one programming language to another, which can be useful when migrating or reusing code in different contexts.
- Automated Testing: Deep learning can be used to automate the testing process, helping in test case generation, test coverage analysis, and identifying test scenarios that are most likely to uncover defects.
Now, let us analyze some real-time examples of the applications where the developers have incorporated deep learning algorithms to get better results. Here are some of the most important names in this regard:
- Google Translate
- Facebook’s facial recognition system
- Tesla’s self-driving car
- Apple’s Siri
- Amazon Alexa
- Netflix Recommendations
- Microsoft’s Cortana
- Google Photos
The list does not end here, but there are several examples where web developers have taken the help of deep learning to improve their work during the development process or the ongoing processes during the practical implementation of web applications. Deep learning is changing trends and applications at a faster rate than ever.
The deep learning technique is revolutionizing the technical sectors of fields such as data analysis, NLP, automobiles, banking and finance, the medical industry, and much more. The scope of deep learning seems never-ending as its algorithms are getting better day by day and are now applied in almost every tech field. I hope this was a helpful article for the readers.
About the Author
Author Name: Syed Zain Nasir.
Author Bio: I am a Mechatronics & Control Engineer and a part-time hobbyist, who believes that only research & technology can make this world a better place.