Deep learning has become a hot topic in the tech world as it rolls forward, changing the way we live our lives. Deep learning is a subset of machine learning, but it is more advanced and deep learning means a machine can actually self-correct. Deep learning and machine learning are both sets of artificial intelligence, or AI. These applications focus on learning and detection to help them act more autonomously. Machine learning is an element of artificial intelligence that simply means that a machine is able to learn from its inputs and outputs. Deep learning is a complex set of this machine learning within AI. Deep learning doesn’t require any human intervention—it uses algorithms and large sets of data to find patterns and create outputs, giving answers.
Deep learning utilizes neural networks which are connections made to mirror the human brain. This is how a computer is able to learn and adapt based on the information it receives.
If you’re studying for a future in information technology, it’s crucial to understand deep learning and Ai, and how they impacting the IT world. This guide will help you learn more about how deep learning and artificial intelligence works, careers associated with deep learning, and how we see it all around us today.
The history of deep learning, machine learning, and AI dates back to the 1940’s when Warren McCulloch and Walter Pits created a computer model for neural networks, all based on algorithms called threshold logic. This was the first attempt to replicate human neural networks with a machine. In 1958 Frank Rosenblatt created an algorithm that focuses on pattern recognition and utilized two-layer neural networks, and proposed that additional layers could be used. In 1980 Kenihiko Fukishima proposed a hierarchal, multilayered artificial neural network that could be used for handwriting and other pattern recognition. In 1989 scientists were able to create algorithms that used these deep neural networks, but the systems needed days to train, making it impractical for use.
In the 2000’s after AI and machine learning had begun to take real form in programs and applications around the world, the term “deep learning” began to gain popularity after research showed how a neural network with many layers could be trained one single layer at a time. In 2009, it was discovered that with a large enough data set, the networks don’t need to be pre-trained, and that the error rates go down. By 2012, human-level performance was achieved with certain deep-learning tasks, and large companies were investing millions of dollars to develop their deep learning capabilities.
Since the 2000’s, companies have started to collect massive amounts of data on their customers and their habits, often called big data. This amount of data had never been seen before, and it was impossible for companies to be able to learn anything from all of this information. Deep learning and AI applications became a key way for companies to be able to parse through their data, computing to find outliers, identifying patterns, and making connections, ultimately answering questions and bringing their big data into digestible pieces. Today organizations thrive on big data and its analysis, learning how to market and produce products and services that their customers want and need. Without big data, companies are flying blind with how to connect to their audience and give them the services they need. And without AI applications, organizations wouldn't be able to understand their big data at all.
Deep learning uses artificial neural networks (ANN) in order to work. These neural networks function like the human brain. The neural networks take in information, and then generate an output based on knowledge and examples. Algorithm applications help the neural networks adapt and learn, without having to regularly be reprogrammed. Each neuron or node inside a neural network is responsible for solving a small part of the problem. They pass on what they know to the other nodes, until all the interconnected nodes are able to solve the problem and offer an output. Trial and error applications are used inside the neural networks to help the neurons solve the problem. Information comes in, flows between the connected neurons where algorithms are used to help solve the problem, and then the solution comes out of the output neuron layer, giving a final prediction or solution.
Machine learning may have a few layers of neural networks—deep learning has hundreds or thousands of layers. These layers are needed to handle the large amounts of big data that comes through. Without enough layers, there aren’t enough neurons to help solve the problem that comes in. Sometimes deep neural networks use hidden layers as part of their algorithms and computing.
Deep learning is able to work with unstructured and unlabeled data—this is data that doesn’t come in neat rows like an Excel document. It’s a mix of files and text and audio clips, all in different areas with no cohesive structure to it. Deep learning is able to take all the information you may have on a customer, from their email subscribe form and path on a web page, to the things they put in their shopping cart and the ads they watched, and find ways to organize and synthesize it. This is crucial for big data sets, as they generally are a bit messy and jumbled, without structure and labels. Machine learning often utilizes structured data, neat packets of information that are already synthesized, in order to work more quickly.
Machine learning utilizes supervised and unsupervised learning—supervised learning is where the machine is graded or corrected on outputs by humans, and then is able to take those corrections and make adjustments. Some machine learning models are capable of unsupervised learning, but it's much less common. They usually are computer-aided to help them get training data, cutting down on time-consuming learning. Deep learning models are capable of unsupervised learning, where it is able to learn by itself when things are wrong and make needed adjustments and corrections without interventions.
The reason deep learning applications are able to learn and adjust is because it utilizes hierarchical learning. Higher-level, more abstract data and features are defined in terms of lower-level, less abstract features. This helps the algorithms and machines take new data that is harder to parse, and categorize it into areas that are easier to understand and have already been used.
There are a wide variety of applications for deep learning—in fact it surrounds us every single day. Some of the common deep learning applications include:
Self-driving cars. Deep learning models are key in self-driving car technology to help the vehicles be prepared for millions of scenarios that come on the road every day. This application helps prepare vehicles to know how to behave when they are on the road, and learn from the situations they are in.
Language processing. Natural language processing uses deep learning models to help understand all the nuances of grammar and syntax that are part of language speaking and understanding. Deep networks are reading and listening to language in order to learn how to understand and come up with appropriate responses. This software is already being used in translation services across millions of devices.
Virtual assistants. When you talk to Siri or Google, you’re talking to a deep learning neural network. Your virtual assistant application uses these networks for speech recognition to understand what you’re asking, recognize your voice, and figure out how to give you what you are looking for. It is always learning and computing to help enhance its effectiveness. An answer to a question, a connection to online shopping, and more are all output responses from your virtual assistant.
Entertainment. When your Netflix offers you a new video to watch, it’s looking into deep networks to learn about you and your preferences, then offer you new movie options. Deep learning allows viewers to get a more personalized entertainment experience. Its detection and learning helps give you a unique experience
Social media. Facebook, Twitter, and your other favorite social media outlets utilize deep learning in hundreds and thousands of ways. From personalizing your feed with content they think you will like, to serving up ads based on what they know about you, these programs are always learning more about you to give you a unique experience. These programs even use facial recognition for images and logins to help you have a streamlined, simple experience. And all of this is possible thanks to deep learning.
Healthcare. Healthcare is another key area where deep learning is essential for innovation and development. Medical imaging is getting more sophisticated as programs utilize deep learning to adapt and understand more about the patient and the images themselves. Drug development is another key area where deep learning is essential, as programs are able to run new scenarios and evaluate new combinations to help find solutions. Medical innovation relies on deep learning to help it move faster and more efficiently than ever before.
The future of deep learning is as vast as you can imagine. New technologies and algorithms are being developed constantly to help computers get more sophisticated. It may be in the future that deep learning changes the way computer memory works, giving us more vast storage options than we ever imagined. Enhanced customer experience, more specific marketing techniques, and more convenience are all part of the future of AI. With these may also come deeper discussions on privacy issues—currently companies can get huge amounts of information about consumers in an instant, and discussions are always happening about privacy and security of information. Deep learning may be able to help enhance cybersecurity, and new regulations will likely have to be enacted about privacy and buying and selling data. All of these areas are likely to be part of the exciting future of deep learning.
If you’re interested in deep learning, there are many career options you could pursue to help you reach your goals including;
Business intelligence developer
If you’re interested in deep learning, all of these careers require a background in IT, and specifically a bachelor’s degree or master’s degree in data analytics could be essential in helping you get the specific knowledge you need to pursue these exciting careers. Consider WGU as you search for a degree program that can get you on the path to an exciting future in deep learning.