Deep learning solutions are shaping the future of mobile app development and data science capabilities. AI technologies and Machine Learning solutions have long been disruptive in application development. However, deep learning models are taking app capabilities and user expectations to levels never seen before.
While deep learning might seem like science fiction, the truth is deep learning technology is already being used to transform the User Experience and deliver analytic insights in several different applications. You have likely used deep learning technology in the past few days. That is how widespread its use is now.
If your business wants to gain a competitive advantage and deliver an incredible User Experience to its customers, you should explore the logistics of implementing deep learning technologies. This post will explain what deep learning is, how it works, and some of the ways this technology is being used every day in a variety of different applications.
What Is Deep Learning?
Deep learning is a type of Artificial Intelligence (AI) and Machine Learning that attempts to mimic how humans learn particular things. Deep learning is a subset of Machine Learning characterized by three or more layers of neural networks. Traditional Machine Learning algorithms are linear. Deep learning algorithms are stacked in layers of increasing complexity and abstraction.
While deep learning techniques try to simulate the activity of the human brain, this technology is still far from being able to match its ability. Nevertheless, deep learning is a critical part of data science and has been effectively used in predictive analytics and modeling. Data scientists frequently use deep learning models to collect, analyze, and interpret massive amounts of raw, unstructured data. The simplest definition of deep learning is a way to automate predictive analytics.
How Does Deep Learning Work?
To begin understanding how deep learning works, imagine a toddler whose first word is “cat.” A toddler learns what is and is not a cat by saying the word and pointing at various objects and animals. Typically, a parent or teacher will confirm, yes, that is a cat, or no, that is not a cat. The more objects and animals a toddler sees, the more aware that toddler becomes of what features and qualities a cat possesses.
This simple example of a toddler learning what a cat is, is an example of clarification of a complex abstraction. Deep learning models learn how to identify things much the same way a toddler learns to identify a cat. The more data a deep learning solution is given, the more accurate its output will be.
Traditionally, a data scientist has to supervise the learning process with Machine Learning algorithms. The observer must be specific when telling the algorithm what to look for. As a result, the algorithm’s success depends entirely on the ability of the data scientist to accurately define the feature set. Deep learning offers superior performance to Machine Learning because it builds the feature set independently without requiring supervision.
The unsupervised learning made possible through neural networks is more efficient and accurate than supervised learning. A data scientist will typically feed the model training data to train deep learning models. For example, the training data might include several images labeled “cat” and “not a cat.” The deep learning solution takes this training data, creates a feature set, and builds a predictive model based on the data.
As the predictive model analyzes more data, it becomes more complex and accurate. In the example of the toddler learning the word “cat,” it will take them weeks or even months to fully understand the concept of the word cat. On the other hand, a deep learning solution given a training set on cats can sort through millions of images within minutes and accurately identify which ones are cats.
Deep learning services not only require access to massive amounts of computing power, but they also require large training sets. However, since deep learning models can generate complex predictive models based on their own iterative output, deep learning solutions are great for analyzing vast amounts of raw, unstructured data.
Deep Learning Services Being Used Everyday
After learning more about the complexities of deep learning, you might be surprised to learn just how frequently deep learning and other types of AI are used in our daily lives. The truth is deep learning is often so integrated into services and products that we are unaware we are even using these complex services.
Perhaps one of the most common applications of deep learning is image recognition. Not only is image recognition used to help unlock our smartphones, but it is used in our favorite social media and content creation apps too. Another prime example of deep learning in our daily lives is natural language processing and speech recognition.
Have you ever called a business and interacted with a chatbot that instructed you to speak about the service or issue you are having? This is an example of deep learning. How about translation apps? The ability to translate speech and text is also powered by deep learning. Have you ever seen a black and white photo from the past that has been colorized? In the past, a person would painstakingly perform the colorization process by hand, but now deep learning models can quickly colorize black and white images.
In addition to all of the ways we interact with deep learning services every day, these models are also being used behind the scenes in MedTech, FinTech, law enforcement, and industrial applications to make a safer, more secure world for everyone.
The Limitations of Deep Learning
Deep learning is a wonderful tool for society, and it will help make our world a better place to live. However, deep learning does have its limitations. The major limitation of deep learning is data. Models trained with incomplete data or even biased data will produce flawed or biased results.
Amazon discovered that a deep learning model it was working on to vet applications for software development and other technical positions was biased towards women. The model itself wasn’t biased initially, but because it was given biased training information, it learned to prefer men to women when it came to specific positions. Amazon quickly scrapped this program, but it illustrates the potential flaws associated with AI and deep learning.
Final Thoughts
Deep learning represents incredible possibilities for our society and your business. You can implement deep learning models to analyze large amounts of unstructured data and improve the services your digital assets provide to users. While this technology might seem like it is out of your reach, the reality is that it is not. If you’re interested in how deep learning solutions can benefit your business, reach out to an app development partner to learn more.
