Deep learning is an intelligent machine’s way of learning things.
It’s a learning method for machines, inspired by the structure of the human brain and how we learn.
It’s a critical technology that makes autonomous vehicles a reality and is also the reason why your smartphone’s voice assistant gets better at assisting you with time. In other words, deep learning is our best shot at creating machines with human-like intelligence.
What is deep learning?
Deep learning (DL) is a sub-field of machine learning that imitates the functioning of the human brain in processing data. DL enables machines to learn without human supervision and grants them the ability to recognize speech, translate languages, detect objects, and even make data-driven decisions.
In other words, deep learning is a type of machine learning inspired by the structure of the human brain. In effect, DL is an imitation of the neurons of the human brain and tries to mimic their functions.
Although deep learning is a branch of machine learning (ML), DL systems aren’t restricted by a finite capacity to learn like traditional ML algorithms. Instead, DL systems can learn and improve their performance with access to larger volumes of data.
Deep learning allows artificial intelligence systems to imitate the manner in which humans acquire certain kinds of knowledge. DL algorithms try to draw conclusions – similar to how humans do it – by continually analyzing data. To achieve this, DL uses artificial neural networks (ANNs).
DL imitates the working of the human brain, mainly the functions such as processing data and creating patterns for decision-making. It’s interesting to note that scientists and AI researchers started building ANNs so that machines could eventually exhibit the characteristics of human intelligence, such as problem-solving abilities, self-awareness, perception, creativity, and empathy, to name a few.
Deep learning wouldn’t have been possible without computers getting cheaper, faster, and smaller. The same is true for storage devices as large amounts of data need to be stored and processed for deep learning to become a reality. That’s why although deep learning was theorized back in the 1980s, it became feasible only recently.
quintillion bytes of data are generated by humans every day.
Processing such enormous volumes of unstructured data is virtually impossible for humans. Even if we do manage to acquire the needed manpower, it might take years to analyze and extract relevant information from those large datasets. However, with deep learning, this process is astonishingly simplified.
With the help of deep learning, an AI system can learn and improve without any human supervision. DL also enables machines to learn from data that is unlabeled or unstructured, or both. However, do note that the learning process can be unsupervised, semi-supervised, or supervised.
Deep learning is also a critical part of data science. It is beneficial for data scientists to collect, analyze, and interpret large volumes of data and makes processes like predictive modeling faster and more efficient.
Branches of artificial intelligence such as computer vision and natural language processing are practicable due to deep learning. Before we get into that further, let’s look at how deep learning works to help us.
How does deep learning work?
In simpler terms, DL’s learning process takes place by modifying the system actions based on a continuous feedback loop. The learning system is rewarded for every right action and punished for the wrong ones. The system tries to adjust its actions to maximize the reward.
Deep learning uses supervised, semi-supervised, as well as unsupervised learning models to train.
The neurons that form the neural networks can be classified into three categories based on their hierarchy: input, hidden, and output layers.
- The input layer, which is the first neuron layer, receives the input data and passes it to the first hidden layer.
- The hidden layers perform specific computations, like image recognition, on the received data.
- Once the computations are complete, the output layer generates the requisite output.
As previously mentioned, deep learning is made possible by artificial neural networks. They’re built by drawing inspiration from the neural networks of the human brain. A massive number of perceptrons – the artificial counterpart of neurons – are stacked together to form ANNs.
The term “deep” is used to specify the number of hidden layers the neural networks have. While traditional neural networks contain two to three hidden layers, deep networks can have even 150 layers.
An easy way of understanding how deep learning works is by looking at convolutional neural networks (CNNs). It’s one of the most popular types of deep neural networks other than recurrent neural networks (RNNs), generative adversarial networks (GANs), and feedforward neural networks.
CNN extracts features directly from the images, eliminating the need for manual feature extraction. None of the features are pre-trained; instead, they are learned by the network when it trains on the given set of images. This automated feature extraction characteristic makes deep learning models highly effective for object classification and other computer vision applications.
The reason why deep neural networks are highly accurate in identifying features and classifying images is due to the hundreds of layers they hold. Each layer would learn to identify specific features, and as the number of layers increases, the complexity of the learned image features increases.
Deep learning vs. machine learning
Machine learning is an application of AI that enables machines to learn and advance automatically from experience, without being explicitly programmed to do so.
The spam filtering algorithm present in your email account is an excellent example of a machine learning algorithm. ML algorithms are also used in OTT platforms like Netflix to recommend movies and series you’re more likely to watch and enjoy.
ML algorithms are capable of analyzing data, identifying patterns, and making predictions. They learn and adapt as newer datasets are introduced to them. In a way, machine learning makes computers more human as it grants the ability to learn and progress.
As mentioned earlier, deep learning is a subset of machine learning, which in turn is a subset of artificial intelligence. More specifically, deep learning is actually machine learning and can be considered an evolved version of the latter. Quite often, many use DL and ML interchangeably as they function almost similarly.
However, their capabilities are different. Although ML algorithms can learn and improve gradually, they still need some form of guidance. For instance, if the algorithm makes an incorrect prediction, then human intervention is essential to make adjustments. On the contrary, deep learning algorithms can determine whether their predictions are accurate or not with the help of artificial neural networks.
The AlphaGo program developed by DeepMind extensively uses deep learning. It’s the very first computer program to beat a human professional Go player. AlphaGo was succeeded by numerous advanced versions, including MuZero, which can master a game without being taught the rules.
It’s interesting to note that researchers have tried to use traditional machine-learning techniques to train robots to master the game for many years. But they succeeded only when they combined deep learning with reinforcement learning and other paradigms.
Another way to differentiate between machine learning and deep learning is by looking at how they learn. Suppose you have to teach a machine to categorize the images of dogs and cats. If you’re using the machine learning model, you’ll have to provide structured data – in this instance, the labeled images of dogs and cats – for the algorithm to learn the specific features that differentiate the images of both the animals. The algorithm gets better with each labeled image exposed to it.
|Machine learning||Deep learning|
|Human supervision||Required||Not required|
|Labeled data||Required||Not required|
|Training time||Seconds or a few hours||Hours or a few weeks|
|Computational resources||Lesser resources needed||Massive resources needed|
If you’re using the deep learning model, you don’t necessarily have to provide structured data or labeled images in this case. The artificial neural networks can help algorithms understand distinct features of each animal.
Once the images are processed through different layers of the deep neural networks, the system will have access to specific identifiers, which will help in classifying the animals and their images. The different output processed by each layer of the neural network is combined to categorize the images effectively.
The presence of neural networks also means that deep learning algorithms require large datasets. That’s because the DL algorithms can learn only when exposed to a million or above data points. On the other hand, ML algorithms can learn and improve with pre-defined guidelines.
Another notable difference between machine learning and deep learning is the type of hardware required for both. Since the complexity of calculations and the amount of data being processed is significantly lower for machine learning, ML programs can run on low-end computers without requiring much computational power.
On the other hand, deep learning systems require massive computational resources and powerful hardware components like graphical processing units (GPUs). Computer scientist Andrew Ng determined that GPUs could increase the speed of deep learning systems by more than 100 times.
With the help of GPUs, the time taken to train deep learning models can be cut down from days to just hours. The majority of deep learning frameworks such as PyTorch and TensorFlow are already GPU-accelerated.
Companies like Nvidia have become more serious about GPU-accelerated deep learning and are tweaking their products accordingly. Also, GPUs are useful for matrix or vector computations.
The time taken to train deep learning and machine learning algorithms are also significantly different. As you might have guessed, deep learning algorithms take a lot of time to train due to the massive amount of data and complex calculations involved. It might take a few hours or even weeks to train a DL system, whereas an ML system can be trained in a few seconds or hours.
Again, choosing between deep learning and machine learning should be a highly informed decision. The decision must be made by taking into account the volume and nature of data, the complexity of the problem you are trying to solve, and the computational resources available.
Deep learning applications
Although deep learning is considered a budding field, researchers and organizations are already benefiting from its applications. Here are some deep learning examples that are shaping the world around us, and most probably, you might have come across some of them in your daily life.
Autonomous vehicles are the most famous benefactors of deep learning. Millions of datasets that replicate numerous real-life scenarios are fed into the system, which is used to teach the vehicle how to navigate the road safely.
With the help of deep learning models, manufacturers can ensure that driverless cars can handle unprecedented scenarios without causing harm to the riders or pedestrians.
Along with helping machines solve hypothetical scenarios, deep learning also helps them analyze and process the raw data collected from cameras, GPS, and numerous sensors. Doing so allows the autonomous vehicles to identify and distinguish between lanes and road dividers, barricades, signs, pedestrians, slowing or halted cars, and more.
Natural language processing
Natural language processing (NLP) is a field of artificial intelligence that grants machines the ability to understand, interpret, and derive meaning from human languages. Simply put, NLP makes it possible for machines to converse with humans and even understand the contextual nuances of a language.
Smart assistants like Siri and Google Assistant and language translation apps like Google Translate are real-world examples of NLP. NLP can be further broken down into natural language generation (NLG) and natural language understanding (NLU).
At first glance, speech recognition may seem just a matter of converting sound to respective words. It’s pretty simple for humans as our brain’s auditory cortex has been trained for years to recognize and understand a speaking language or more.
A simple example to represent the complexity of understanding sounds is “recognize speech” and “wreck a nice beach.” Both sound very similar, although their meanings are entirely different. Although machines can detect words in a sentence, understanding their contextual meaning is still a herculean task. That’s where DL comes in for NLP.
Almost all smart assistants rely on deep learning, and their understanding and accuracy levels are increasing with each task. Google Assistant, which depends almost entirely on DL, has the highest accuracy.
Deep learning also allows machines to understand the complexities of a language, such as tonal nuances, expressions, and even sarcasm. Understanding the complexities of a language is also critical for sentiment analysis on textual data. Only then can companies monitor brand and product reputation, understand public opinion, and analyze customer experiences.
Another application of deep learning is document summarization. Document summarization or simply text summarization is the task of extracting crucial information from a large text passage and creating a concise synopsis of it. Along with saving time for humans, document summarization can also help computer programs that need to process large amounts of data within a short period of time.
Speaker recognition is another useful application of deep learning and is becoming increasingly accurate. Governments can use this technology to identify terrorists making anonymous phone calls by matching their voice samples against a database containing recognized voices.
Before deep learning, the field of image recognition heavily relied on manual tuning. This means a lot of processes had to be performed by humans and took a lot of time. Deep learning eliminates the need for manual or traditional image processing and significantly fastens the entire process.
In this decade, the majority of accurate object detection systems you come across rely solely on deep learning. Google Photos is an excellent example. It uses deep learning to classify and group images.
Even if you haven’t done any manual labeling, you can search your Google Photos album for something like “insects on flowers” and get results, given you have related images stored. You can even search for animals based on their species or breeds and still get all photos containing the particular animal.
While traditional non-deep learning systems have a hard time identifying the objects of an image, deep learning goes several steps ahead. It does an impressive job of recognizing human faces, animals, places, and things with high accuracy and almost zero error.
With the introduction of the Internet of Things (IoT), factories are getting smarter than ever. Automation isn’t new to the manufacturing industry, and deep learning makes things more streamlined.
With the help of deep learning architectures like CNN, companies can replace the majority of human operators who were otherwise integral to spot defective products in the assembly line.
This way, spotting quality issues becomes more accurate and cost-effective, and chances of human error are eliminated. Such systems are also highly scalable and can be trained to detect quality issues at any point in the production line.
Another application of deep learning in manufacturing is predictive maintenance. By collecting and analyzing the health data of machinery over a period of time, deep learning algorithms can predict the chances of a manufacturing asset breaking down.
Determining when to repair a piece of equipment is critical from a company’s financial standpoint as a faulty machine could halt the entire production. Since irregular maintenance can also cause costly, irreparable damage to machines and catastrophic factory accidents in the worst case scenario, companies can save a lot with predictive maintenance. Knowing when to repair will also help companies plan ahead and look for alternatives to reduce factory downtime.
Factory input optimization is another beneficial application of deep learning. With consumers becoming more concerned about the carbon footprint of products and the eco-friendly reforms made by their creators, companies have no choice but to optimize the usage of physical resources.
Plus, optimizing resources will help companies profit more from each product, hence factory input optimization. By tracking resource usage (electricity and water consumption, mainly) of different machinery and production processes, deep learning systems can dynamically suggest best optimization practices.
Drug discovery is incredibly time-consuming and expensive. Deep learning can make this process cheaper and faster. Deep learning can help predict the binding affinity of drugs with particular proteins and even the toxic effects of specific compounds.
AtomNet is a deep convolutional neural network used for rational drug design. It’s a state-of-the-art technology capable of finding novel and non-obvious drug compounds and can be a remarkable tool for accelerated drug repurposing projects. AtomNet was also used to predict new candidate biomolecules for Ebola and multiple sclerosis (MS).
Hospitality is a multibillion-dollar industry always eager to adopt new technologies, and deep learning technology is no exception. With DL, organizations can find new means to enhance customer experience and satisfaction and even identify costly, replaceable processes.
Deep learning can help organizations plan ahead by predicting seasonal demands. A deep learning system can effortlessly find the correlation between factors that cause seasonal demands and predict future trends by analyzing past data.
By analyzing customer data, DL models can also help companies build customer strategies for better retention and satisfaction rates. Companies can also use various machine-learning techniques for competitive pricing by considering multiple factors such as seasonality, real-time events, third-party promotions, local events, and past booking data.
Since processing complex big data is a specialty of deep learning, it has immense potential in the financial industry. By analyzing historical data, various market parameters, and external factors that may affect a company’s performance, deep learning algorithms can predict stock values with impressive accuracy.
Since DL algorithms can analyze vast volumes of data from multiple sources simultaneously, it is unbelievably faster than humans and so is used to create profitable trading strategies.
Deep neural networks are also used in the loan approval process. By analyzing historical data regarding approval and rejections, banks can rightly assess the risks of approving a loan to an entity.
Image restoration is another impressive feat deep learning can pull off. Image restoration generally refers to the recovery of a clear non-degraded image from a degraded image. Degradation can occur due to a number of factors, with image noise being one.
If image noise is the culprit, then the process of restoration is called image denoising. Similarly, images can be of lower resolution, and by the process of super-resolution, higher resolution images can be created.
With deep learning, such processes of restoration become more accurate and less time-consuming. Learning methods such as Deep Image Prior are utilized for the restoration process. In simple terms, Deep Image Prior is a convolutional neural network used to enhance an image without any prior training data other than the image itself.
In 2017, the Google Brain team researchers trained a deep neural network to analyze very low-resolution images of faces and predict the faces. This method is called Pixel Recursive Super Resolution and can significantly enhance the resolution of images. The neural network can pinpoint the distinguishing features of a person with ease.
Deep learning is also extensively used to colorize black and white photos. You can check out online tools like Algorithmia to see how specific black and white images would have looked if taken with a color camera.
Deep learning allows mobile advertisers to publish ads that can capture their target audience’s attention and deliver a higher return on investment (ROI). Deep learning techniques such as data-driven predictive advertising is used to increase the relevancy of ads as well.
Numerous real-time bidding mobile ad networks use deep learning APIs, which help advertisers maximize click-through rate (CTR). The faster response times of deep learning systems also enables advertisers to serve the right ads at the right time and space.
Detect developmental delays
Early diagnosis and treatment of developmental disorders, autism, or speech disorders can positively impact a child’s future. A human wouldn’t notice numerous early-stage signs, but a deep learning system indeed can.
Using deep learning, Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory and Massachusetts General Hospital’s Institute of Health Professions have created a computer system that can identify speech disorders even before a child enters kindergarten.
Also, children who are on the autism spectrum struggle to recognize the emotional states of the people around them. For instance, children with autism will have a hard time differentiating between a happy and fearful face.
As a remedy to this issue, some doctors use deep learning-powered, kid-friendly robots to engage children in imitating emotions and responding to them in appropriate ways. As the robot interacts, it analyzes the child’s interest and engagement by looking at their responses.
Deep learning allows the robot to extract the most crucial information from the data collected without needing any human assistance. With the help of DL, researchers uncovered numerous fascinating facts such as the cultural differences between children of different countries.
They observed that during episodes of high engagement, children from Japan showed more body movements. On the other hand, large body movements were linked with disengagement episodes for children from Serbia.
One of the biggest reasons this type of treatment is effective is that the robot is primed to attract children’s attention. Also, humans tend to change their expressions frequently and express the same emotion in different ways. But the robot always does it in the same manner so that the learning process will be much less frustrating for the child.
Sound production is an integral part of filmmaking. Although certain sounds like footsteps, knocking on the door, or screeching tires can be borrowed from stock audios, many a time, they have to be recreated to enhance the cinematic experience.
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have created a deep learning algorithm that predicts sound. When given a silent video clip of an object being hit, the algorithm can produce realistic sounds. The predicted sound is realistic enough to fool humans.
To train the algorithm, researchers filmed roughly 1,000 videos of nearly 46,000 sounds that constitute different objects being hit, prodded, and scraped with a drumstick. They used a drumstick specifically because it offered a consistent method to produce a sound.
Sound prediction systems will not only make things better for the film industry but could assist intelligent machines in navigating the world and understanding the properties of objects.
Have you ever tried translating foreign languages with the Google Translate app? Not only does the app “translate” the words, but it overlays the image with the translation. The app does this with the help of deep neural networks and is one of the many ways Google squeezes deep learning into a smartphone.
Once the app finds where the letters are located in the image by analyzing its pixels, a convolutional neural network trained on letters and non-letters tries to recognize what each letter is. Once the letters are identified, the app looks up in a dictionary to get translations.
The translation is then rendered on top of the original letters in the same style as the original image. Such visual translations are super fast if performed on Google’s data centers. But since the majority of users own a low-end smartphone and have unstable internet connections, Google developed a tiny neural network with numerous limitations.
Deep learning algorithms are used in recommendation systems to suggest content users are more likely to watch. These algorithms’ effectiveness is critical for platforms like Netflix as only if users frequently find interesting content, they’ll continue the subscription. Amazon and numerous other e-commerce platforms also rely heavily on deep learning algorithms to recommend the right products and boost sales.
Fraud-related losses and damages are a sad reality of the financial industry. Financial scammers are growing.
A lot of fraudulent activities can be detected with the help of rule-based systems. For instance, large transactions or the ones that happen in unusual places are great indicators of fraud and can be easily detected.
However, there can be numerous user behaviors that rule-based systems may not identify as suspicious, but DL-based fraud detection systems surely would. The processing power for DL-based systems is also remarkable, and they also reduce the need for manual work – unlike rule-based systems that require frequent human supervision and manual corrections.
How to create and train deep learning models
There are three common ways in which you can train a deep learning model to perform object classification. You could either train it from scratch, transfer learning, or use a network as a feature extractor. Let’s take a quick look at each.
1. Training from scratch
To train deep neural networks from scratch, you need to acquire large volumes of labeled data sets – for example, the labeled images of cats and dogs. After that, you need to design a network architecture that can learn the distinct features of the animals. Depending on the volume of data, rate of learning, and processing power, the networks might take days or weeks to train.
2. Transfer learning approach
The most common way of training deep neural networks is by the transfer learning approach. In this process, a pre-trained model is fine-tuned to perform a new task. You can start off with an existing network and feed new datasets containing previously unknown classes to it.
You can tweak the network according to your requirements, in this case, identifying and distinguishing between the images of cats and dogs. Since this process requires less amount of data, the computation time drops significantly.
3. Using feature extractor
Another approach to train a deep learning model is to use a network as a feature extractor. Since each layer of the network is designated to learn specific features from images, you can actually pull these features from the network during the training process. These features can then be inputted into a machine learning model. Doing so can reduce the need for enormous computational resources.
Deep learning: the more, the better
An interesting property of deep learning is that it gets better if you provide more data and more computational resources. Although deep learning algorithms may seem too demanding, they’re highly accurate and require less to no human assistance in most cases.
Deep learning will also be our key to unlocking artificial general intelligence, an AI system capable of thinking, learning, and acting like humans.
Learn more about artificial general intelligence and see for yourself whether such an intelligent machine would be a friend or a foe.