Introduction
Deep learning is a variety of machine learning and artificial intelligence that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modelling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier.
There are various tasks like multiplying big numbers or search operations, which are difficult for human beings to perform but are easy to perform on computers, and there are certain tasks like driving or language conversations that are tough to be performed on machines. Machine learning is used to make computers perform tasks which can be done better by human beings. Machine learning is the use of computer algorithms, which enables the machine to learn to access the data automatically with an improved experience. It has made life easy and has become an essential tool in many sectors like agriculture, banking, optimization, robotics, structural health monitoring, etc. One of the machine learning approaches that are dominant in these fields of applications is deep learning.
Technical Impact of Deep Learning
Machine learning may require human intervention when the desired output is different from the desired solution. Deep learning systems learn by sending the information through layers in the artificial neural network. These networks are capable of identifying their mistakes and rectifying them without human intervention. The probability of error in deep learning is possible only if the input or data sets are poor.
Deep learning can assist in learning the internal regularity and representation levels of training data, and the information obtained is of great help for interpreting the data. Deep learning has led to many achievements in object detection, image segmentation, and classification applications.
Deep learning has provided a breakthrough in the field of Artificial intelligence, which had limitations in the past decades. Deep learning has provided methods to classify millions of images into a lesser number of classes, thereby reducing the error percentage. Scene labelling is used to label every pixel of the image to categorize the body into the class it belongs to. Body poses of human beings can be estimated in the images by using different network combinations for modelling joint relationships. Different architectures can be used to optimize the computational costs for deep learning methods for image recognition.
Advantages of Deep Learning models
Deep learning models can lead to better, faster and cheaper predictions which lead to better business, higher revenues and reduced costs.
Better predictions: Which business wouldn’t want to be able to call just the customers who are ready to buy or keep just the right amount of stock? All of these decisions can be improved with better predictions.
Faster predictions: Deep learning, and machine learning in general, automates a company’s decision making increasing its execution speed. Consider customers that leave their contact info to get more details about a tech solution for their company. Maybe it is obvious from the contact info that this is a very high potential and needs to be contacted. Thanks to the model in place, no one needs to manually check that data, the potential customer will be immediately prioritized. Speed is especially important in this example because customers contacted sooner are more likely to convert.
Cheaper predictions: Companies that do not implement operational decision-making models, rely on analysts to make decisions which are orders of magnitude costlier than running deep-learning models. However, deep learning models also have setup time and costs. Therefore, the business case for model needs is investigated before rolling out models.
Popular fields of applications
Virtual 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 with a secondary human interaction experience. Virtual assistants use deep learning to know more about their subjects ranging from your dine-out preferences to your most visited spots or your favourite songs. 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. Virtual assistants are literally at your beck and call as they can do everything from running errands to auto-responding to your specific calls to coordinating tasks between you and your team members. With deep learning applications such as text generation and document summarizations, virtual assistants can assist you in creating or sending appropriate email copies as well.
Visual Recognition
Imagine yourself going through a plethora of old images taking you down the nostalgia lane. You decide to get a few of them framed but first, you would like to sort them out. Putting in the manual effort was the only way to accomplish this in the absence of metadata. The maximum you could do was sort them out based on dates but downloaded images lack that metadata sometimes. Deep Learning and now images can be sorted based on locations detected in photographs, faces, a combination of people, or according to events, dates, etc. Searching for a particular photo from a library (let’s say a dataset as large as Google’s picture library) requires state-of-the-art visual recognition systems consisting of several layers from basic to advanced to recognize elements. Large-scale image Visual recognition through deep neural networks is boosting growth in this segment of digital media management by using convolutional neural networks, Tensorflow, and Python extensively.
Fraud Detection
Another domain benefitting from Deep Learning is the banking and financial sector which 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 fraud 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 and identifying anomalous behaviour 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.
Photo Descriptions
Computers tend to automatically classify photographs. For instance, Facebook creates albums of tagged pictures, mobile uploads and timeline images. Similarly, Google Photos automatically label all uploaded photos for easier searches. However, these are merely just labels. Deep Learning takes to another level and several steps forward. It can describe every existing element in a photograph. A work that was executed by Andrej Karpathy and Li Fei-Fei, trained a Deep Learning network to identify dozens of interesting areas in an image and write a sentence that describes each of them. This means that the computer not only learnt how to classify the elements in the photograph but also managed to describe them with English grammar.
Conclusion
Deep learning involves complex scientific concepts and algorithms used in Artificial Intelligence. It continues to evolve with the ever-present expansion of digital information circulating throughout cyberspace. For all that deep learning has to offer, it requires massive computational power and training datasets that limit its ability to be applied across new domains.
However, the advancement of deep learning represents how far we’ve come to achieving real machine intelligence. For the problems we are looking to solve in business and automation, deep learning is still the best bet.