Deep learning continues to be a marvel in today’s world with all the possibilities it brings. In this guide, we shall be looking at deep learning and its applications in our world today. Read through this guide if you are inquisitive enough to know what deep learning is.
What is Deep Learning?
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
Furthermore, Deep learning has aided image classification, language translation, and speech recognition. It can be used to solve any pattern recognition problem and without human intervention.
Artificial neural networks, comprising many layers, drive deep learning. Deep Neural Networks (DNNs) are such types of networks where each layer can perform complex operations such as representation and abstraction that make sense of images, sound, and text. Considered the fastest-growing field in machine learning, deep learning represents a truly disruptive digital technology, and it is being used by increasingly more companies to create new business models.
How deep learning works
Computer programs that use deep learning go through much the same process as a toddler learning to identify a dog. Each algorithm in the hierarchy applies a nonlinear transformation to its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep.
In traditional machine learning, the learning process is supervised, and the programmer has to be extremely specific when telling the computer what types of things it should be looking for to decide if an image contains a dog or does not contain a dog. This is a laborious process called feature extraction, and the computer’s success rate depends entirely upon the programmer’s ability to accurately define a feature set for the dog. The advantage of deep learning is the program builds the feature set by itself without supervision. Unsupervised learning is not only faster, but it is usually more accurate.
Initially, the computer program might be provided with training data — a set of images for which a human has labeled each image dog or not a dog with metatags. The program uses the information it receives from the training data to create a feature set for dogs and build a predictive model. In this case, the model the computer first creates might predict that anything in an image that has four legs and a tail should be labeled a dog.
Example of Deep Learning at Work
Let’s say the goal is to have a neural network recognize photos that contain a dog. All dogs don’t look exactly alike – consider a Rottweiler and a Poodle, for instance. Furthermore, photos show dogs at different angles and with varying amounts of light and shadow. So, a training set of images must be compiled, including many examples of dog faces that any person would label as “dog,” and pictures of objects that aren’t dogs, labeled (as one might expect), “not dog.”
The images, fed into the neural network, are converted into data. These data move through the network, and various nodes assign weights to different elements. The final output layer compiles the seemingly disconnected information – furry, has a snout, has four legs, etc. – and delivers the output: dog.
Now, this answer received from the neural network will be compared to the human-generated label. If there is a match, then the output is confirmed. If not, the neural network notes the error and adjusts the weightings. The neural network tries to improve its dog-recognition skills by repeatedly adjusting its weights over and over again. This training technique is called supervised learning, which occurs even when the neural networks are not explicitly told what “makes” a dog. They must recognize patterns in data over time and learn on their own.
After learning what is Deep Learning, and understanding the principles of its working, let’s go a little back and see the rise of Deep Learning.
How does deep learning attain such impressive results?
In a word, accuracy. Deep learning achieves recognition accuracy at higher levels than ever before. This helps consumer electronics meet user expectations, and it is crucial for safety-critical applications like driverless cars. Recent advances in deep learning have improved to the point where deep learning outperforms humans in some tasks like classifying objects in images.
While deep learning was first theorized in the 1980s, there are two main reasons it has only recently become useful:
- Deep learning requires large amounts of labeled data. For example, driverless car development requires millions of images and thousands of hours of video.
- Deep learning requires substantial computing power. High-performance GPUs have a parallel architecture that is efficient for deep learning. When combined with clusters or cloud computing, this enables development teams to reduce training time for a deep learning network from weeks to hours or less.
Applications of Deep Learning
Which are common applications of deep learning?
The healthcare sector has long been one of the prominent adopters of modern technology to overhaul itself. As such, it is not surprising to see Deep Learning finding uses in interpreting medical data for
- the diagnosis, prognosis & treatment of diseases
- drug prescription
- analyzing MRIs, CT scans, ECG, X-Rays, etc., to detect and notify about medical anomalies
- personalizing treatment
- monitoring the health of patients and more
One notable application of deep learning is found in the diagnosis and treatment of cancer.
Medical professionals use a CNN or Convolutional Neural Network, a Deep learning method, to grade different types of cancer cells. They expose high-res histopathological images to deep CNN models after magnifying them 20X or 40X. The deep CNN models then demarcate various cellular features within the sample and detect carcinogenic elements.
Personalized marketing is a concept that has seen much action in the recent few years. Marketers are now aiming their advertising campaigns at the pain points of individual consumers, offering them exactly what they need. And Deep Learning is playing a significant role in this.
Today, consumers are generating a lot of data thanks to their engagement with social media platforms, IoT devices, web browsers, wearables, and the ilk. However, most of the data generated from these sources are disparate (text, audio, video, location data, etc.).
To cope with this, businesses use customizable Deep Learning models to interpret data from different sources and distill them to extract valuable customer insights. They then use this information to predict consumer behavior and target their marketing efforts more efficiently.
Another domain benefitting from Deep Learning is the banking and financial sector which is plagued with the task of fraud detection with money transactions going digital. Autoencoders in Keras and Tensorflow are being developed to detect credit card fraud saving billions of dollars of cost in recovery and insurance for financial institutions.
Fraud prevention and detection are done based on identifying patterns in customer transactions and credit scores and identifying anomalous behavior and outliers. Classification and regression machine learning techniques and neural networks are used for fraud detection. While machine learning is mostly used for highlighting cases of fraud requiring human deliberation, deep learning is trying to minimize these efforts by scaling efforts.
Natural Language Processing
NLP or Natural Language Processing is another prominent area where Deep Learning is showing promising results.
Natural Language Processing, as the name suggests, is all about enabling machines to analyze and understand human language. The premise sounds simple, right? Well, the thing is, human language is punishingly complex for machines to interpret. It is not just the alphabet and words but also the context, the accents, the handwriting, and whatnot that discourage machines from processing or generating human language accurately.
Deep Learning-based NLP is doing away with many of the issues related to understanding human language by training machines (Autoencoders and Distributed Representation) to produce appropriate responses to linguistic inputs.
One such example is the personal assistants we use on our smartphones. These applications come embedded with Deep Learning imbued NLP models to understand human speech and return appropriate output.
Deep Learning is the force that is bringing autonomous driving to life. A million sets of data are fed to a system to build a model, train the machines to learn, and then test the results in a safe environment. The Uber Artificial Intelligence Labs at Pittsburg is not only working on making driverless cars humdrum but also integrating several smart features such as food delivery options with the use of driverless cars.
The major concern for autonomous car developers is handling unprecedented scenarios. A regular cycle of testing and implementation typical to deep learning algorithms is ensuring safe driving with more and more exposure to millions of scenarios.
Data from cameras, sensors, and geo-mapping is helping create succinct and sophisticated models to navigate through traffic, identify paths, signage, pedestrian-only routes, and real-time elements like traffic volume and road blockages. According to Forbes, MIT is developing a new system that will allow autonomous cars to navigate without a map as 3-D mapping is still limited to prime areas in the world and not as effective in avoiding mishaps.
Facial Recognition is the technological method of identifying individuals from images and videos by documenting their faces. It uses advanced biometric technology to record a person’s face and match it against a database to extract their identity.
Facial Recognition is an old technology, first conceptualized in the 1960s. However, it is the integration of neural networks in facial recognition that exponentially increased its detection accuracy.
Deep Learning enforced Facial Recognition works by recording face embeddings and using a trained model to map them against a huge database of millions of images.
For instance, DeepFace is a facial recognition method that uses Deep Learning (hence the name) to identify persons with a recorded 97% accuracy rate. It uses a nine-layer neural network for its purpose and has been trained using four million images of about 4000 individuals.
Artificial Intelligence and its subsets are fortifying a lot of industries and sectors, and agriculture is no different.
Of late, smart farming has become an active agricultural movement to improve upon the various aspects of traditional agriculture. Farmers are now using IoT devices, satellite-based soil-composition detection, GPS, remote sensing, etc., to monitor and enhance their farming methods.
Deep Learning algorithms capture and analyze agriculture data from the above sources to improve crop health and soil health, predict the weather, detect diseases, etc.
Deep learning also finds uses in the field of crop genomics. Experts use neural networks to determine the genetic makeup of different crop plants and use it for purposes like
- increasing resilience to natural phenomena and diseases
- increase crop yield per unit area
- breeding high-quality hybrids
In conclusion, although deep learning is a newer technology than machine learning and artificial intelligence, it has lots of applications, even more than were mentioned above. It’s important you begin to learn more about deep learning.