Deep Learning: How It Works And What It Really Is

Remember, how in popular sci-fi movies we often see intelligent machines or humanoid robots living in the world just like any normal human? Or unique cars and transportation systems that don’t require a human to drive. Or probably machines that are capable of thinking just like us. In other words, they could do everything without human intervention. Scientifically, all these feats could be possible one day with a special branch of AI and machine learning, which is deep learning algorithms.

But right now, although computers and technology have covered a long way since its inception, machines still are no match for us when it comes to the processing power of a human brain nor they can do things just like us. Sure, computers today are super-fast and their processing speed is unimaginably quick. But they are still not intelligent enough to do most of the things without human intervention.

What makes us unique is our ability to observe and come up with real solutions without supervision. We can work with a totally new information and decipher the pattern to figure out the most effective way to perform a task.

Deep learning is the branch of machine learning and AI that is trying to replicate the way we perform tasks. The goal of DL algorithms is to take unsupervised information, analyze, decipher, and then complete the task on its own without human help.

So, why is this so important? Why are we trying to make machines replicate us?

In this article, we’re going to discuss what deep learning is, how it works, and how it could help us to make our lives better and safer.

So, without further delay, let’s dive in…

What Is Deep Learning?

Deep learning is an important part of the artificial intelligence and one of the major subset of the machine learning. In simple words, it’s a cluster of highly advanced machine learning algorithms that uses several artificial neural networks or ANN to analyze and understand the given data.

So, what is the final aim of the DL algorithms and ANN?

The final goal is to be abler to teach machines how to perform complex multi-step tasks without the need of programming. However, unlike normal machine learning, deep learning is an effort to take automation to a new level. For example, with DL, we’re trying to make the machine identify and understand complex human behaviors, speech pattern, and to identify different objects.

So, why is this a big deal?

To help you understand, let’s talk about the self-driving cars a bit. It’s no secret that self-driving cars use ML algorithms to operate without human help. However, without object detection, which is a high-level deep learning feature, the car won’t be able to distinguish between a lamp post and a human.

Therefore, it’s quite fair to say that without DL, self-driving cars will never be safe. After all, the last thing you would want for your car is to hit a person thinking it was a telephone post.

But training the DL algorithms is not an easy task and it takes a lot of time and data. Simply, the more data you feed your DL program with, the more efficient and accurate it will become.

How Does Deep Learning Work?

The process of deep learning is a combination of two separate stages. These stages are:

Training.
Inferring.

In the training stage, the DL program is supplied with a huge amount of data. After that, the program tries to sort the data out and find the common characteristics. After that, the program memorizes all the data and their work pattern so it can make the most accurate decision when it faces the similar set of data in the future.

This training process usually have these important phases:

The artificial neural network asks a large number of true or false based questions.
It extracts the numerical values from the large amount of data it gets.
After it receives all the answers, the program then classifies all the data accordingly.
Last but not least, the program then label all the data it has received and processed.

Meanwhile, in the process of inferring, the deep learning algorithm comes to the most suitable conclusion. Also, it repeats the training stage for all the new data it receives during the inferring stage and lebel them accordingly.

Deep Learning Vs. Machine Learning: What’s The Difference Between Them?

In machine learning, the user normally provides the program with a set of data and examples of the data that help the program come to the right conclusion. Obviously, the system is bound to make a few errors that the human user correct later to help the system become more efficient. In other words, while classical machine learning is capable of performing a huge amount of tasks, it will eventually need human help.

Deep learning, however, takes the machine learning to a new level where it requires far less human interference. And unlike ML, the DL might even create new features instead of just identifying the features of the supplied data.

Here are some of the biggest differences between ML and DL that you need to know:

DL usually needs tons of unrefined data for training and labeling so it can make the right decisions. On the other hand, ML can function after receiving just a small amount of data.
DL programs usually require exceptionally high-performing hardware to function at their full capacity. However, ML can work with low-spec machines.
The ML program often needs human help to learn to make the correct decision in the training phase. Meanwhile, DL creates its own new features and requires less human interference.
It takes a lot more time to train a deep learning program compared to its machine learning counterpart.
Machine learning programs usually break a single task down into smaller pieces, solve each of them, and then combine the solved tasks come to the conclusion. Meanwhile, DL programs solve a problem as it is without breaking it down.
DL doesn’t provide enough transparency for the decisions it makes. The user often stays in the dark without properly understanding how the program solved the problem. However, with ML, you will get step-by-step data on how it solved the problem with a lot of transparency than DL.

The Advantages Of DL

In 2016, the GVR compiled a report where they estimated the global market cap for the deep learning technology. And according to the report, the market cap for DL tech was about $272 million dollars in 2016. And a huge portion of this market belonged to the military and defense industries as well as the drone industry.

The same report also suggests that the market cap of the deep learning tech is increasing exponentially over the last few years. And it will continue to grow at this rate and reach around $10.2 billion in the market cap by the end of 2025.

But what is causing such mind-blowing growth of DL technology? What’s so special about it?

To find the answer, you need to dive deep into the advantages that come with this amazing technology. In this section, we will discuss the advantages of using deep learning technology.

Creating New Features

One of the biggest advantages of DL technology over general machine learning programs is its ability to create new set of features from the existing features of its supplied datasets for training. So, in simple words, unlike machine learning, deep learning programs can create new tasks on its own to solve a problem. So, what does it really mean?

It means that now we have a program that can create its own set of variables and system characteristics without human or third party interference. This will not only save company’s and organizations’ time while working with big data, but it will also help them work with and solve complex problems without human help that wasn’t possible before.

Advanced Analysis

While classical machine learning can only work with a supervised set of data, deep learning algorithms have the capability to learn from random or unsupervised data on its own. And its ability to create new features and variables will help us solve complex problems and run a reliable analysis of extremely difficult problems with techniques far superior to our current ones.

A Few Examples Of The Practical Use Of Deep Learning

If data is the new petroleum of the humanity, then deep learning is our new oil refiner. Thanks to DL algorithms, we could analyze data way faster than before without human intervention. It means we can automate many of our daily tasks. In fact, according to a report on automation, companies who are using deep learning technology to automate their tasks are going to add around $1.8 trillion of extra revenue.

So, in this section, we are going to show you some of the example of the practical use of DL that is trying to make our lives safer and better.

Self-driving Vehicles

DL is extremely important to make self-driving vehicles a reality. Thanks to the object recognition feature of deep learning’s ANN program, the vehicle can distinguish not only between different objects, but it can also understand the difference between stationary and moving objects and calculate the best driving rout.

But how does the DL program do it?

Simple. First it captures the data of the road using cameras and sensors fixed into it. And then it quickly analyzes all the data to find to best rout to continue and adjust the steering wheel and other controls accordingly.

It’s important to note that 5G technology and smart high-performance devices play a big role in the success of quick processing of the data as DL requires extremely advanced hardware and fast network with minimal latency. Since 5G has an extremely low latency rate which is under 10ms, many companies has already experienced success in their test of automated vehicles.

Industrial Automation

DL algorithms can be utilized to automate and optimize supply chain quickly and efficiently. Moreover, it can be used for automated maintenance, manufacturing, and repairing saving companies both time and money. In fact, several companies such as Siemens and KUKA have already been using DL for their manufacturing work.