All you need to know about Deep Learning !

When we talk about Deep Learning we also have to understand Machine Learning and Artificial Intelligence as all three are interrelated to each other. Machine learning is something more emphasized in developing applications that can learn from data through the use of Artificial Intelligence. This application improves the accuracy and efficiency of data learning over time without any human interference. Deep learning is one step ahead of this process which believes in the smart use of artificial intelligence in large data learning processes. To understand deep learning in details, we need to understand

What is Deep Learning? 

In deep learning, artificial neural networks are used for providing actions to the application for further reaction. Therefore artificial neural network systems can be inferred as the human brain. As the human neural network provides particular action to the human brain likewise deep learning also stimulates the human brain to perform particular action or task. This overall process helps any application of a device to deeply learn the data for better assessment and analysis which is further used for various activities.

Why is deep Learning getting popular nowadays?

In recent decades, Artificial Intelligence has gained significant rise and attention among emerging technology giants. Even in our daily lives, the role of various AI-based applications has increased significantly. The performance of AI applications are really inspiring and it has made a huge impact on advanced technology. But gradually, we are moving ahead with technology and we require more data to be worked on. When we move towards large amounts of data we also need to consider the accuracy and efficiency. For that, we have to take the advantage of Deep Learning which has a strong power in terms of accuracy.

Difference between Machine Learning and Deep Learning

There are various points where we can differentiate between ML and DL as

·      Deep learning is a part of Machine Learning whereas Machine Learning is a process in which a particular device or application is trained and equipped to read and learn the data

·      Machine learning works on broader term whereas deep learning is basically for in-depth learning system of programme or application

·      In a deep learning process, relevant features from data are automatically extracted from images. Deep learning performs “end-to-end learning” where a network is given raw data and a task to perform. Similarly, in Machine Learning, automatic extraction functions are not embedded

Process of Deep Learning

The process of Deep learning is moreover similar to how the human brain works. The neural network of deep learning works like the human brain. Deep learning neural networks are known as deep neural networks. The process of deep learning includes the steps like data processing, reprocessing, refining, analysis, result, recognition, classification and presentation of data within the object. In the whole process, the data is processed accurately to collect more reliable information and result.

The deep neural network is a combination of various layers of interconnected nodes. Every node has a different deep learning algorithm to perform the action of identification of features. These nodes analyse the possibility of how the object could be identified, classified and learned finally.

The deep learning process has two main components in the form of two layers as input and output layers. These layers are known as visible layers. The data in the deep learning process enters from the input layer whereas final assessed data after complete processing gets out from output layers. The final processing of data like identification, classification, or description, happens at the output layer. There are some hidden layers also in between visible layers where the processing of more complex data takes place. The movement of data from input to output layers for processing and calculation is called forward propagation.

In a similar node, the process identifies errors that are processed or calculated predictions and sends them back to previous layers for refining. This step is called backpropagation. Both forward and backpropagation work simultaneously and it allows the network to make predictions during the data learning process. The whole process is designed to get more accuracy and efficiency in the deep learning process of large amounts of data.  The process of deep learning looks simple but it’s very complex and has multiples of operations in it.

Major applications of Deep Learning

Although the use of deep learning applications is booming rapidly, in most cases we cannot even imagine the application in which it’s integrated. Most of the time, deep learning applications are integrated into the product itself. 

Investigation and detection

In many cases, police collect massive amounts of video and audio details of any criminal activity but it’s very difficult to identify the possible evidence of the episode. The deep learning programming/ algorithms system can easily read, learn and analyze the data and provide the details of criminal activities being taken place. This system can also read and learn the details of images, documents that are more accurate and fast. 

Banking and Finance

Deep learning applications have become common in banking and finance companies. Now every company is regularly using predictive analytics to calculate the trading of stocks, risk in business, detect fraud, and help to manage credit and investment portfolios for clients.

Client Support Services

Today, whenever we visit any website, a chat box opens up automatically which is the live example of AI in Clint support services. After the incorporation of deep learning, now this chatbox has come up with advanced features like chat in many languages, auto-reply, auto questions etc. This chat box works like a human resource for companies to deal with clients on a virtual platform. Now with new innovations, these chat boxes are updated into audio, video, speech recognition features also.

Medical Industry

The use of deep learning has played a crucial role in the medical industry as it has helped to detect the images of a particular disease. The image recognition application is being used to keep the record of medical staff, patients, the progress of treatment, and extent of disease to be cured etc. It also helped the hospital management to analyze the particular situation and timely action-taking. 

Automatic Car System

Many companies are now moving forward with Automatic cars or driverless car systems. In the Automatic car system, deep learning is being used to automatically detect certain objects like traffic signals, traffic lights, crowds, pedestrians etc. Also, the image detection application of these devices can reduce the possibility of accidents to a large extent.

In space science

Deep learning is also being used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for research teams.

Disease Research

In a way to expand deep learning applications, the use of deep learning in the research of fatal disease may be a game-changer. Many countries are using deep learning applications in research work of complexities like cancer, paralysis, mutation, syndrome etc. The application is now embedded with an electronic microscope for the fine detection of DNA sequences from large data sets of DNA with more accuracy. In other cases, deep learning applications are being used to identify the cancer cells in the body.

Industrial Safety

Nowadays, the safety of industry personnel, labour has become a big concern for companies. In this sense, deep learning may be helpful to provide advanced safety features to companies. The deep learning embedded smart devices can automatically detect the risk possibility in machinery and can be aware of the management for timely precautions or maintenance. 

Electronics Devices 

Deep Learning programmes/ applications are also used in auto-hearing and speech translation devices nowadays. These devices are powered with Deep Learning and used to provide the preferences of voices to consumers. The consumer can use these devices as security features in their home.

Advertising 

Advertising is another area where we can see the massive use of deep learning. Both publishers and marketers are using it to increase the relevance of their commercials and raise their promotional strategies to return on investment. It has massive use in social media advertising 

Automatic Colorization 

The process of applying colour to pictures is termed image colourization. Deep learning can be used to colour a picture using the objects and their background within the photograph, just as the problem might be approached by a human operator. This power optimizes Image Net-trained and co-opted high-quality and very large fully convolutional neural networks for image colourization.

   Handwriting Generation

   In this operation, a collection of handwriting samples, new handwriting for a given word or phrase are generated and provided to the application. The handwriting is given after the handwriting samples are produced as a series of coordinates used by a pen. The relationship between the pen movement and the letters is learned from this collection, and fresh examples can be produced ad hoc. It can also help detectives to identify the signature theft in money laundry cases. 

          Unique features of Deep Learning :

·      A major benefit of deep learning is powered by vast amounts of data and it is the reason that it has become popular

·    In most of the cases, all features of an application need to be defined by a domain specialist in standard machine learning methods. It reduces the sophistication of the data and makes patterns more apparent for working with learning algorithms. The greatest benefit of Deep Learning algorithms is that they strive to learn high-level features from data in a gradual way. This removes the need for domain knowledge and the extraction of hard core features

·   The problem-solving approach is another major distinction between deep learning and machine learning strategies. Deep learning techniques aim to solve the problem end to end, where the problem statements need to be broken down into different pieces to be solved first as machine learning techniques and then their outputs need to be merged at the final level

          Usually, due to large numbers of parameters, a data learning algorithm takes a long time to train. It may take about two weeks to train entirely from scratch with the popular ResNet algorithm. Whereas, it takes a few seconds to a few hours to train classical machine learning algorithms. In the testing stage, the scenario is completely reversed. The Deep Learning algorithm takes even less time to run test time.

·         A key advantage of deep learning networks is that they often continue to improve as the size of your data increases in machine learning, you manually choose features and a classifier to sort images. With deep learning, feature extraction and modelling steps are automatic.

·         If the data size is high, Deep Learning Outperforms other techniques. However, standard Machine Learning algorithms are superior to limited data sizes.

·         Techniques for deep learning ought to have high-end resources in order to be trained in a decent period of time

·         When it comes to complex problems such as image classification, natural language processing, and voice recognition, Deep Learning really outperforms all the other methodologies

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all-about-deep-learning

Future of Emerging Technology in the 21st Century 

The Revolution of Information Technology took place in the latter half of the 20th century.  After that, the use of the internet, computer and communication technology became common. Now everything is available on the internet and it has changed the lifestyle of everyone. Even the survival of human beings has become difficult without the internet and technology.  According to reports, technology marketing is rising by 42% every year and in the next 5 years, there will be 150 million technological jobs globally. 

Now we have moved into the 21 century and the technology revolution is on pace. Many new technologies are coming in every sector and it is shaping the future of innovations. Some of these emerging technologies are following 

  • Artificial Intelligence, Machine Learning and Deep Learning 
  • Data Science & Analytics
  • Digital Marketing 
  • Business Analytics
  • Internet of Things (IoT)
  • Cybersecurity

Artificial Intelligence, Machine Learning and Deep Learning 

As the role of technology is increasing in every sector, the importance of AI and ML has also been increased simultaneously. The demand for Artificial Intelligence professionals is also increasing rapidly and it has witnessed 32 % growth in recent years. Artificial Intelligence systems can be associated or customized according to the needs of any application.  

On other hand, Machine learning which is the subset of Artificial Intelligence is also rising as we are moving towards more advanced technology. It has the capacity to reprogramme itself with big data and higher accuracy. Machine Learning has a huge role in technologies like automatic cars, robotics, security devices, online fraud detection, accident detection, image detection etc. 

Deep learning is one step ahead of AI and ML which is used to learn big data with more accuracy and efficiency. It is a subset of Machine learning and teaches itself to perform particular tasks with any human interference. When we move towards the huge no we also have to ensure accuracy with technology and in that case, deep learning is very helpful. 

Data Science and Analytics 

Data science has become one of the hottest jobs now. Today, every competent company relies on Data Science to gain business insights and in order to extract the required information from the data, they need Data Scientists. This information is used to predict the growth of business and make needful planning accordingly. This is the reason that now companies are searching for experienced and skilled Data Scientists. Data Science applications cover the operations like data collection, data mining, data analysis, data visualization, data validation, reporting, making prediction etc. Data Scientist and Data Analytics have a huge role in the growth of business for any company. 

Digital Marketing

In business, marketing is all about connecting with people in the right place and right time. But in the age of the internet, where everybody is busy with electronic gadgets like mobile, laptop, computers, it is very difficult to find customers physically. In this case, we have to meet the audience where they are busy most of the time on the internet. Digital Marketing enables you to reach a larger audience through the internet where they are most likely to purchase your product. Digital Marketing is often more cost-effective than traditional advertising media. Additionally, Digital Marketing enables you to measure performance on a daily basis which is not possible in traditional marketing. Today, digital marketing is important for any business and brand image. As every company has its website but if they don't have any social media presence they can’t survive for a longer time. Major ways of digital marketing are through email, video, social media, and search engines.The various tools of digital marketing are Search Engine Optimization, Content Marketing, Social Media Marketing, Pay Per Click (PPC), Email Marketing.

Business Analytics 

In today’s world, almost every organization has a substantial abundance of data, but all of this data remains unprocessed in the storage. Likewise, every company makes no strategy to predict the growth of its business through this data. There are many tools to analyse the growth of their business but only the companies that have expert Business Analytics can be able to use this data. Due to this, every company is in search of expert business analysts who can understand the data and make needful strategies for their business. This thus has increased the demand of professionals in Business Analytics. 

Internet of things (IOT)

In simple words, the Internet of things (IoT) is a network of interrelated and internet-connected objects without human intervention. It is the association of the internet connected objects like sensors, software and other technologies. We can connect devices from small to higher size through IoT. Through IoT, we are able to acquire and transfer data over a wireless network. One of the most important aspects of IoT devices is that we are able to generate a huge amount of information. IoT is majorly used in technologies such as Artificial Intelligence and Deep Learning. The use of IoT can be understood from instance, your fitness tracker watch can track calories, voice recording and can even be connected with a smartphone even. 

Cyber Security

Gradually, we are moving towards digital transformations, the threat of cyber attack is always there. As everything is available on the internet, the fear of data leaking, data manipulation, data privacy, and bug attacks are major concerns for companies nowadays. Due to it, cybersecurity is one of the serious concerns of organisations today and it also increases the demand of Cyber Security Professionals.

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Ashpreet Kaur - Jul 2, 2021

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