Deep Learning is currently the buzzword in corporate circles. It underlies several technologies like search engines, email replying systems, voice recognition, and self-driving cars. But given the rapid pace of technological advancements, many business owners and senior executives are still in the dark about what deep learning is exactly and how it can be useful. Read on to find out more about deep learning and its connection to machine learning, neural networks, and real-world applications.
Deep Learning & Machine Learning
Though Deep Learning and Machine Learning (ML) are often used interchangeably, they are distinct. To understand deep learning, it’s important to examine ML. Machine Learning is most commonly described as a type of AI where computers learn to do something by being ‘trained’ and not ‘programmed’ to do it.
In the process of this training, the ML program constructs a model, based on historical data and tests it with new data inputs. The program evaluates how well it did at identifying the new data and uses this information to adjust the model so it will do better on the next try. The program continues this iterative process until it has built a model that delivers highly accurate results.
This training is often done with deep learning methods. Deep learning algorithms use multiple layers of nonlinear processing units for feature extraction and transformation. The algorithm ‘learns’ using pattern analysis or classification methods.
In deep learning, the program learns something from the data on each level and transforms it into a more abstract and composite representation as it is passed through multiple layers. For example, in image recognition, what is initially presented to the program is raw data in the form of pixels. With deep learning, each layer will recognize and encode each part of the image, for instance, the eyes, the ears etc., with the final layer recognizing that the image has a face.
Basic ML requires a human programmer to identify whether the output is correct. On the other hand, due to its multi-layered structure, deep learning can estimate the accuracy of its answers by itself. However, some level of fine-tuning of layers by programmers will be required to help the model deliver accurate results.
Neural Networks and Deep Learning
Most modern deep learning models are based on what is called an Artificial Neural Network (ANN). The ANN is a framework for different ML algorithms to process complex data. These ANN computing systems are inspired by the biological neural networks that make human and animal brains. So, the concept of learning in computing systems, in a way, mimics the human learning process through the neural network. Using the training methods of deep learning as outlined above, artificial neural networks can be used to solve complex problems of prediction and classification. Artificial neural networks that use deep learning have several applications such as speech recognition, machine translation, social network filtering, and medical diagnosis.
Why Deep Learning is important
Deep learning has several applications in speech/voice technology, Natural Language Processing (NLP), big data, data-based prediction among other areas. In today’s big data-driven environment, deep learning can be used in a variety of industries to produce insights and results.
Speech or voice technology has been rapidly adopted by users across the world – through Google Assistant, Amazon Alexa, Apple Siri, voice search and so on. Even with wearable technology, users now have the option of interacting with it via voice. All these large-scale automatic speech recognition systems are based on deep learning.
Many corporations that work with big data need to use applications with deep learning networks. Deep learning can be effectively used in tasks that require supervised and unsupervised learning to derive insights. This is essential for those corporations that have massive troves of unstructured or unlabeled data. Another commercial use, for example, is a retail store using a deep learning app that is designed for image recognition and tagging. This allows customers who may have seen the store’s product on social media, for instance, to use that image rather than keywords to search for it.
Within the area of healthcare, for instance, deep learning is being used in clinical tools. Methods like Convolutional Neural Networks (CNN) are well suited to analyze images like those in MRI scans and X-rays. As a tool for physicians, it can help deliver faster and more accurate diagnoses.
Deep learning is already impacting us on a daily basis when we talk to our smartphones or our connected devices in our smart homes. The applications of deep learning will continue to expand as machine learning grows, and as big data is increasingly used to drive business decisions.