Deep Learning is a Machine Learning Technique that teaches computers/machines to imitate humans and therefore the way they think or react to a particular problem set. Deep Learning concerned with algorithms inspired by the structure and function of the brain called Artificial Neural Networks (ANNs).
Until recently, it was difficult to perform such tasks due to a setback in computing powers but now 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.
Evolution of Deep Learning
It is believed that Deep Learning was invented at the dawn of 21st-century, but believe it or not, it has originated since the 1940s.
The reason most of us are unaware of Deep Learning advancements that were developed in the 20th century is due to the fact that the approaches used then were relatively unpopular due to their various shortcomings and the fact that it had a couple of revitalization since then.
There were THREE Waves:
- Cybernetics: During 1940–1960
- Connectionism: During 1980–1990
- Deep Learning: Since 2006
The first 2 waves were unpopular due to the critics of their shortcomings, however, there is no doubt that it has helped advance the field to where it is today and some of the algorithms developed during those times are used widely till today in various Machine Learning and Deep Learning models.
After two dips, the third wave emerged in 2006 with a breakthrough. The advancements by Geoffrey Hinton were used by other researchers to train different types of Deep Networks. This enabled researchers around the world to Train Deeper and Deeper Neural Networks and led to the popularisation of the term Deep Learning.
Why Deep Learning over traditional Machine Learning?
Machine Learning is a set of algorithms that parse data, learn from them, and then apply what they’ve learned to make intelligent decisions. They require a lot of domain expertise, human intervention and can only perform tasks for what they have been designed, nothing more nothing less.
Deep Learning is a subset of Machine Learning that achieves great power and flexibility by learning to represent the world as a nested hierachy of concepts. With each concept defined in relation to simpler concepts, and more abstract representations computed in terms of fewer abstract ones.
Problem-solving in Deep Learning
Deep Learning permits machines to unravel advanced issues even when employing a dataset that’s extremely numerous, unstructured, and inter-connected. The additional Deep Learning Algorithms they learn, the additional they perform. The process of problem-solving in Deep Learning does not want to be broken down into small steps. It solves problems on an end-to-end basis.
Applications of Deep Learning:
Some of the major applications of Deep Learning are:
Self-driving cars: Deep Learning is the force that is bringing autonomous driving to life. A million sets of data are fed to a system to build a model, to train the machines to learn, and then test the results in a safe environment. A regular cycle of testing and implementation typical to Deep Learning algorithms is ensuring safe driving with more and more exposure to millions of scenarios. Data from cameras, sensors, geo-mapping is helping create succinct and sophisticated models to navigate through traffic, identify paths, signage, pedestrian-only routes, and real-time elements like traffic volume and road blockages.
Healthcare: One of the chief Deep Learning applications in healthcare is the identification and diagnosis of diseases and ailments which are otherwise considered hard-to-diagnose. This can include anything from cancers that are tough to catch during the initial stages, to other genetic diseases. Deep Learning and Machine Learning are both responsible for the breakthrough technology called Computer Vision. One of the most sought-after applications of Machine Learning in healthcare is in the field of Radiology which enables medical image analysis at any particular time.
Voice assistants: The most popular application of Deep Learning is virtual assistants ranging from Alexa to Siri to Google Assistant. Each interaction with these assistants provides them with an opportunity to learn more about your voice and accent, thereby providing you a secondary human interaction experience. They learn to understand your commands by evaluating natural human language to execute them. Another capability virtual assistants are endowed with is to translate your speech to text, make notes for you, and book appointments.
Fraud detection: Another domain benefitting from Deep Learning is the banking and financial sector that is plagued with the task of fraud detection with money transactions going digital. Autoencoders in Keras and Tensorflow are being developed to detect credit card frauds saving billions of dollars of cost in recovery and insurance for financial institutions. Fraud prevention and detection are done based on identifying patterns in customer transactions and credit scores, identifying anomalous behavior, and outliers. Classification and Regression Machine Learning techniques and neural networks are used for fraud detection. While Machine Learning is mostly used for highlighting cases of fraud requiring human deliberation, Deep Learning is trying to minimize these efforts by scaling efforts of the machines. Machine Learning allows for creating algorithms that process large datasets with many variables and help find these hidden correlations between user behavior and the likelihood of fraudulent actions. Another strength of the Machine Learning system compared to rule-based ones is faster data processing and less manual work. For example, smart algorithms fit well with behavior analytics for helping reduce the number of verification steps.
Source: Becoming HumanRelated posts: