What is Deep Learning?

Artificial Intelligence and machine learning are the cornerstones of the next revolution in computing. These technologies hinge on the ability to recognize patterns then, based on data observed in the past, predict future outcomes. This explains the suggestions Amazon offers as you shop online or how Netflix knows your penchant for bad 80s movies. Although machines utilizing AI principles are often referred to as “smart,” most of these systems don’t learn on their own; the intervention of human programming is necessary. Data scientists prepare the inputs, selecting the variables to be used for predictive analytics. Deep learning, on the other hand, can do this job automatically.

Deep learning can be considered as a subset of machine learning. It is a field which is based on learning and improving on its own by examining computer algorithms. While machine learning uses simpler concepts, deep learning works with artificial neural networks which are designed to imitate how humans think and learn. Until recently, neural networks were limited by computing power and thus were limited in complexity. However, advancements in big data analytics have permitted larger, sophisticated neural networks, allowing computers to observe, learn and react to complex situations faster than humans. Deep learning has aided image classification, language translation, 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 type 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.

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How Deep Learning Does What it Does

Neural networks are comprised of layers of nodes, much like the human brain is made up of neurons. Nodes within individual layers are connected to adjacent layers. The network is said to be deeper based on the number of layers it has. A single neuron in the human brain receives thousands of signals from other neurons. In an artificial neural network, signals travel between nodes and assign corresponding weights. A heavier weighted node will exert more effect on the next layer of nodes. The final layer compiles the weighted inputs to produce an output. Deep learning systems require powerful hardware because they have a large amount of data being processed and involves a number of complex mathematical calculations. Even with such advanced hardware, however, deep learning training computations can take weeks.

Deep learning systems require large amounts of data to return accurate results; accordingly, information is fed as huge data sets. When processing the data, artificial neural networks are able to classify data with the answers received from a series of binary true or false questions involving highly complex mathematical calculations. For example, a facial recognition program works by learning to detect and recognize edges and lines of faces, then larger parts of the faces and, finally, the overall representations of faces. Over time, the program trains itself and the probability of correct answers increases. In this case, the facial recognition program will accurately identify faces with time.

Let’s say the goal is to have a neural network recognize photos that contain a dog. Clearly, 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 which 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 moves 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.

How Did Deep Learning Come About?

Machine learning is said to have occurred in the 1950s when Alan Turing, a British mathematician proposed his artificially intelligent “learning machine.” The first computer learning program was written by Arthur Samuel. His program made an IBM computer improve at the game of checkers the longer it played. In the decades that followed, various machine learning techniques came in and out of vogue.

Neural networks were mostly ignored by machine learning researchers, as they were plagued by the ‘local minima’ problem in which weightings incorrectly appeared to give the fewest errors. However, some machine learning techniques like computer vision and facial recognition moved forward. In 2001, a machine learning algorithm called Adaboost was developed to detect faces within an image in real time. It filtered images through decision sets such as “does the image have a bright spot between dark patches, possibly denoting the bridge of a nose?” When the data moved further down the decision tree, the probability of selecting the right face from an image grew.

Neural networks did not return to favor for several more years, when powerful graphics processing units finally entered the market.The new hardware enabled researchers to use desktop computers instead of supercomputers to run, manipulate and process images.. The greatest leap forward for neural networks happened because of the introduction of great amounts of labelled data with ImageNet, a database of millions of labelled images from the Internet. The cumbersome task of manually labelling images was replaced by crowdsourcing, giving networks a virtually unlimited source of training materials. In the years since, technology companies have made their deep learning libraries open source. Examples include: Google Tensorflow, Facebook open source modules for Torch, Amazon DSSTNE on GitHub and Microsoft CNTK.

Deep Learning in Action

Aside from your favorite music streaming service suggesting tunes you might enjoy, how is deep learning impacting people lives? As it turns out, deep learning is finding its way into applications of all sizes. Anyone using Facebook cannot help but notice that the social platform commonly identifies and tags your friends when you upload new photos. Digital assistants like Siri, Cortana, Alexa and Google Now use deep learning for natural language processing and speech recognition. Skype translates spoken conversations in real time. Many email platforms have become adept at identifying spam messages before they even reach the inbox. PayPal has implemented deep learning to prevent fraudulent payments. Apps like CamFind allow users to simply take a picture of any object and, using mobile visual search technology, discover what the object is.

Google, in particular, is leveraging deep learning to deliver solutions. Google Deepmind’s AlphaGo computer program recently defeated standing champions at the game of Go. DeepMind’s WaveNet can generate speech mimicking human voice that sounds more natural than speech systems presently on the market. Google Translate is using deep learning and image recognition to translate voice and written languages. Google PlaNet can identify where any photo was taken. Google developed the deep learning software database, Tensorflow, to help produce A.I. applications.

Deep learning is only in its infancy and, in the decades to come, will transform society. Self-driving cars are being tested worldwide; the complex layer of neural networks are being trained to determine objects to avoid, recognize traffic lights and know when to adjust speed. Neural networks are becoming adept at forecasting everything from stock prices to the weather. Consider the value of digital assistants who can recommend when to sell shares or when to evacuate ahead of a hurricane. Deep learning applications will even save lives as they develop the ability to design evidence-based treatment plans for medical patients and help detect cancers early.

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Career Prospects

The field of artificial intelligence is seriously understaffed. While not all companies are currently hiring professionals with deep learning skills quite yet, having such trained experts are expected to gradually become a crucial requirement for organizations looking to remain competitive and drive innovation. Machine learning engineers are in high demand because neither data scientists nor software engineers has precisely the skills needed for the field of machine learning. The role of machine learning engineer has evolved to fill the gap. What is deep learning promising in terms of career opportunities and pay? Quite a bit. Glassdoor lists the average salary for a machine learning engineer at nearly $115,000 annually. According to PayScale, the salary range spans $100,000 to $166,000. Growth will accelerate in coming years as deep learning systems and tools improve and expand into all industries.

Learn More About Deep Learning

There has never been a better time to be a part of this cutting-edge technology. If you are interested in entering the fields of AI and deep learning, you should consider Simplilearn’s tutorials and training opportunities. Tensorflow is an open source machine learning framework and learning its program elements is a logical step for those on a deep learning career path. Education and earning the right credentials is crucial to develop a trained workforce and help drive the next revolution in computing.

About the Author

Kate ReyesKate Reyes

Kate is a Content Marketing Associate for Simplilearn with more than five years of experience in content creation and strategic marketing. Skilled at technical writing, Kate leverages her passion for storytelling to develop purpose-driven pieces. Kate holds a degree in Communication from Kennesaw State University.

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