What is deep learning in AI and where can it be applied?

In this rapidly changing world, there are times when machines do the work of humans, and the need for complex software to keep them running is increasing day by day. The field of AI is something where machines can perform tasks that normally require human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without any human involvement.

What is deep learning?

The way a machine learns certain aspects of data and solves them on its own is known as deep learning. It involves complex programming and a lot of data.


Computer deep learning algorithms follow a process similar to that of a child learning to recognize a dog. Each algorithm in the hierarchy performs a nonlinear transformation on its input before generating a statistical model on the output. Iterations continue until the output is precise enough to be valid.

Deep learning methods

Deep learning is based on a structured model that works perfectly to deliver results. Some methods are:

The learning rate decrease method – also known as learning rate annealing, or adaptive learning rate, is a technique for increasing performance while reducing training time. Techniques to facilitate the learning rate over time are the most accessible and common learning rate changes throughout training.

Transfer learning – This procedure involves fine tuning of a previously trained model and requires access to the internals of a network. First, users submit new data, including classifications unknown before, into the existing network. You can perform new tasks with more specific categorization skills once the network has been adjusted. This method has the advantage of requiring much less data than the others, which allows the computation time to be reduced to a few minutes or hours.

Learn from scratch – This method involves collecting a large set of labeled data and configuring a network architecture capable of understanding the characteristics and pattern. This method is particularly beneficial for new applications and those with a large number of output categories. However, this is a less typical strategy as it requires a large amount of data, leading to training taking days or weeks.

Benefits of deep learning

The advantages of deep learning are as follows:

No need for feature engineering

One of the most important benefits of using a deep learning approach is that it can perform tasks that don’t require feature engineering. In this method, an algorithm examines the data for the correlated qualities, then combines them to encourage faster learning without being expressly instructed. Since it does not require human intervention, it saves data scientists a lot of time and effort.

Precise results

Humans become hungry or exhausted, and they occasionally make reckless blunders. This weakness is not the case when it comes to neural networks. When properly trained, a deep learning model can perform thousands of repetitive tasks and activities in a fraction of the time it takes a human to do the same. address, the quality of work never decreases.

Deep learning applications

There are many typical applications of deep learning in artificial intelligence. Some of them are:

Estimated travel time

Usually, a single trip takes longer than usual as it involves multiple modes of transportation and traffic schedules to get to the destination. Cutting down on commute time isn’t easy yet, but read on to see how machine learning helps cut commute time.

Google maps: Google Maps can check the frequency of moving traffic at any time using the location data of smartphones, and it can also aggregate the traffic reported by users, such as construction, traffic and accidents. Google Maps can reduce travel time by recommending the fastest route using relevant data and proper feeding algorithms. This example is one of the most common applications of deep learning in artificial intelligence that we use in our daily life.

Driving Apps: From pricing a trip and reducing wait time, to coordinating your trip with other passengers to reduce diversions, driving apps can help. Machine learning is, indeed, the answer. The startup uses machine learning to estimate the cost of a ride, calculate the best pick-up location, and ensure the ride takes the fastest route possible.

Spam filters

Some rule-based filters are not actively served to an e-mail inbox, for example, when a message contains the words “online consultation”, “online pharmacy”, or “unknown address”.

ML provides a powerful feature that filters emails based on various signals, such as words in message and message metadata (like who sent the message, where it is sent from). Although it filters emails based on “daily deals” or “welcome messages” etc., ML is used in this case.

Smart response

You’ve probably noticed how Gmail prompts you to reply to emails with basic phrases like “Thank you”, “Okay” and “Yes, I’m interested”. When ML and AI analyze, estimate, and think about how one counts over time, those responses are personalized with every email.

Intelligent personal assistants

When it comes to personal assistants, there are plenty of options available, ranging from Siri and Cortana to Google Assistant and Amazon Alexa, and Google Home.

By fully implementing AI, these home devices and personal assistants respond to commands like setting a reminder, finding information online, controlling lights, and more.

These personal devices and assistants, like ML chatbots, rely on ML algorithms to collect information, understand a person’s preferences, and improve the experience based on previous interactions with that person.

Banking sector

Fraud Prevention: In most cases, daily transaction data is so large that it becomes difficult for humans to review each transaction manually, so how do you know if it is fraudulent? To solve this problem, AI-based systems that learn what types of transactions are fraudulent are being developed. Businesses use neural networks to detect fraudulent transactions based on factors such as the most recent transaction frequency, transaction size, and type of retailer.

Credit decisions: When applying for a credit card or loan, financial institutions must make a quick decision on whether or not to accept the application. And, if the proposal is accepted, what specific conditions should be included in the offer, such as interest rate, line of credit amount, etc. Financial organizations use machine learning algorithms to make credit decisions and assess risks for individual customers.

Assessment of tests

Plagiarism Detection: Machine learning can be used to create a plagiarism detector. Many colleges and universities require plagiarism checkers to assess students’ writing skills.

Similarity functions that result in a numerical estimate of the similarity of two articles are the algorithmic essence of plagiarism.

Essay grading was once a difficult chore, but researchers and organizations are now developing AI systems that can score essays. A human reader and an e-Rater, a Robo-reader, rate the tests on the GRE.

If the score differs significantly, you can consult a second human reader to resolve the deviation.

Conclusion

Deep learning has gone from a fad to an essential technology that many companies in many industries use on a regular basis. As its scope expands, so do the growth opportunities for your career. Therefore, join machine learning and artificial intelligence courses can help you get there. Great Learning offers a variety of courses from beginner to advanced level.

It is safe to say that the impact of deep learning will be felt in the future in various high-end technologies such as Advanced System Architecture and Internet of Things. More meaningful contributions to the more incredible business world of connected and intelligent products and services are to be expected.

What is deep learning in AI and where can it be applied?