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Deep learning is an advanced form of machine learning. Deep learning refers to the ability of computer systems, known as deep artificial neural networks, to learn from data autonomously using algorithms. These algorithms recognize identifying characteristics of the data.
An artificial neural network is designed to mimic a biological neural network using input layers, output layers, and multiple hidden layers containing mathematical functions called neurons.
Deep learning can be used for pattern analysis on unstructured, unlabeled data as well as on data that has previously been classified or labeled. By continuously feeding lots of training data into these artificial neural networks, the algorithms help progressively improve the network’s performance on a range of tasks, including object recognition, natural language processing, and bioinformatics.
Deep learning platforms have a diverse range of applications in a wide variety of fields. Here are six amazing deep learning implementations you should know about.
1. Computer Vision
One common deep learning use case is image recognition. Image recognition is a part of the broader field of computer vision. Through the process of computer vision computers can understand images or other visual media at a high-level, automating tasks that the human eye can perform.
Deep learning enhances the process of computer vision. Computer vision can be used in self-driving vehicles, which can classify and detect people and vehicles.
Another exciting example of computer vision is its application in medicine. Once shown images of blood-soaked sponges the computer can accurately detect blood loss during surgeries in real-time, which could save millions of dollars in unnecessary blood transfusions.
2. Online Recommendation Engines
Some of the most popular web and mobile applications on the planet, including Netflix, YouTube and Amazon, are driven by engines that use deep learning to intelligently recommend content and products to customers.
The beauty of this deep learning implementation is that the wealth of user-generated data helps to continuously refine the deep networks that power the recommendation engines. The end result is that users are much more likely to see the content or products that are most relevant to their particular wants and needs.
There are perhaps some ethical implications of the growing power of these engines in terms of driving addictive behavior— ”Netflix binge” has entered into common parlance partly because its intelligent recommendation systems drive people to keep watching their shows. However, there is no denying the overall positive impact that deep learning can have by helping to show people content they find relevant.
3. Voice Assistants
Deep learning neural networks used by organizations such as Apple and Amazon are facilitating improvements in voice assistants. Apple uses deep learning to improve the speech of its voice assistant, Siri so that it talks with greater naturalness, personality and expressivity.
Today, Siri’s responses sound far less robotic than they did just a few years ago. Deep learning can also help voice and virtual assistants like Siri and Alexa to better understand user queries.
4. Medical Diagnosis
Deep learning can improve the diagnosis and treatment of diseases using a host of medical data to recognize the patterns that categorize specific diseases. Deep learning networks are able to learn from unstructured data. These networks can thus be used to help improve disease management by making use of unstructured medical data, such as electronic health records.
Deep learning can also be used to recognize patterns in x-rays, MRIs, and other diagnostic testing tools.
Thanks to deep learning, chatbots can now converse with customers more effectively and help them solve any issues they may have. In our plugged-in world customer expectations are high. Customers expect their problems to be solved immediately and expect customer care teams to be available 24/7. Intelligent chatbots powered by deep learning networks are helping to meet these high expectations.
However, while this implementation is genuinely exciting and shows a lot of promise, there is still work to be done for a chatbot to convincingly pass the Turing test, whereby it can converse in a way that is indistinguishable from that of a human.
6. Predictive Marketing Analytics
Identifying the likelihood of future outcomes based on data, known as predictive analytics, is an exciting implementation of deep learning. This concept often gets discussed in relation to predictive maintenance for manufacturing.
Arguably just as exciting is the potential for predictive marketing analytics. Deep neural networks can be used together with services like those offered at Purple. Such services capture a range of useful data from a company’s guest WiFi networks that can be used to perform predictive marketing analytics. The end result is an improved ability to predict customer behavior and preferences and qualifying leads.
That wraps up this post on six exciting deep learning implementations. These implementations that will have profound impacts going forward as the technologies and available data improve the performance of such systems.