Today, though we will explore the top Deep Learning uses and applications, first, we will have a quick overview of the technology itself. Before that, let’s take a look at the topics covered:
While Deep Learning involves complex algorithms and models, in this blog, we will only be talking about the capabilities of Deep Learning to get you started. If you want to read about the more technical aspect of it, you can check out the Deep Learning Tutorial by Intellipaat.
Check out this video on Deep Learning for beginners by Intellipaat:
Introduction to Deep Learning
Deep Learning is a sub-technique of Machine Learning and is majorly concerned with algorithms. It enables computer systems to behave the way humans would in certain situations, thus, minimizing human intervention in operations and facilitating complete automation.
Deep Learning is what makes it possible for the conception and execution of impressive feats like automatic parking in cars and other achievements that may not have been possible before. Through Deep Learning, models can achieve human-level performance or sometimes even beyond that.
Top 15 Deep Learning Uses
Deep Learning Applications in Natural Language Processing
It is not an easy feat to teach machines the semantics, syntax, expressions, tonal nuances, etc. of a language, all of which take humans themselves years and years of interaction and exposure to various social settings to understand and pick up. Natural Language Processing is all about training machines to do just that through Deep Learning and framing appropriate responses to every scenario.
Examples of best Deep Learning applications in NLP: Document summarization in the legal sphere, classifying text, language modeling, answering questions, Twitter analysis, sentiment analysis, etc.
Deep Learning Applications in Ecology
As a rapidly evolving extension of Machine Learning, Deep Learning has yet to receive the deserved attention in ecology. However, its applications have been found to support the fight against global issues such as climate change, biodiversity loss, increasing demand for ecosystem services, ecological predictions, etc. The easy availability of data and better computing power have made it possible for quantitative approaches.
Examples of Deep Learning in ecology: Aquaculture farming, protection of coral reefs, detecting and analyzing animal population, management and conservation, etc.
Deep Learning Applications in Military
Military systems armed with AI and Deep Learning are efficiently able to handle larger volumes of data, and that makes up a critical part of modern warfare owing to effective computing and decision-making capabilities. During immediate threats, Deep Learning solutions streamline analysis and facilitate quick decision-making through critical insights.
Deep Learning also enables intelligence gathering for better assessment of battle scenarios, and faster aerial or ground analysis. It even helps in understanding enemy behavior and communication.
Examples of Deep Learning in the military: Warfare platforms, cybersecurity, logistics and transportation, target recognition, battlefield healthcare, combat simulation and training, threat monitoring, etc.
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Deep Learning Applications in Virtual Assistance
Virtual assistants, Siri, Alexa, Cortana, Google Assistant, etc., are all very popular applications of Deep Learning. Every interaction with them enables them to learn more about your voice and, therefore, to give you the experience of secondary human interaction.
Deep Learning helps these assistants learn all about your preferences and, in turn, gives you a better and personalized experience by offering suggestions ranging from favorite hang-outs to music.
Examples of Deep Learning applications in virtual assistants: Translating speech to text, note-making, appointment booking and management, auto-responding to specific calls, text generation, document summarizations, composing and sending emails, etc.
Deep Learning Applications in Visual Recognition
Deep Learning allows images to be efficiently sorted through locations detected in them, facial recognition, and events, dates, and many other possible categories. Visual recognition systems contain several basic-to-advanced layers. Large-scale applications in this field through deep neural networks are driving growth in digital media management and observe the implementation of Python, Tensorflow, and convolutional neural networks extensively.
Examples of Deep Learning in visual recognition: Stock photography websites, video websites, automated image organization, visual search for improved product discoverability, image classification for websites with large visual databases, etc.
Deep Learning Applications in Agriculture
Deep Learning has recently entered the agriculture domain and is being applied to various agricultural and food production challenges as a tool that provides high-accuracy results and, in most cases, surpasses traditional image processing techniques.
Examples of Deep Learning techniques used in agriculture: Fruit and plant classification, disease detection and identification, fruit counting, etc.
Deep Learning Applications in Big Data
Deep Learning enables the analysis of enormous unsupervised datasets that proves to be a valuable tool for Big Data Analytics. It is capable of extracting complex patterns from these huge volumes of data, data tagging, semantic indexing, quick data retrieval, and streamlining discriminative tasks.
Sophisticated algorithms based on Deep Learning methods are key to accuracy and efficiency during real-time data processing.
Examples of Deep Learning in big data: Automated extraction of complex data, learning from huge amounts of unsupervised data, simulations, social media, classification, prediction, etc.
Check out the difference between ML, AI and Deep Learning in our comparison blog on AI vs Machine Learning vs Deep Learning.
Deep Learning Applications in Healthcare
Deep Learning is contributing to healthcare by mitigating health risks while bringing down costs. It has found its applications mainly in clinical research in the field of medicine. Deep Learning has the potential to become a handy diagnostic companion for inpatients and trigger alerts during high-risk conditions such as respiratory failure or sepsis.
Few examples of Deep Learning in healthcare: Medical imaging, genome analysis, new drug discovery, early and accurate diagnosis of life-threatening illnesses, augmented clinicians, pathology results and treatment course standardization, prediction of future health risks, etc.
Deep Learning Applications in Entertainment
If you are wondering how OTT platforms such as Netflix and Amazon Prime are providing a personalized experience tailored to your preferences, the answer is Deep Learning. These platforms have the capability to recommend shows that may be of interest to a particular viewer.
Even VEVO is able to use Deep Learning techniques to build cutting-edge data services for its users, artists, record labels, companies, and internal business groups. It can help generate performance-based and popularity- based insights.
Likewise, deep video analysis, audio/video syncing and testing, transcriptions, tagging, content editing, auto-content creation, filmmaking cameras that can study body language, etc. are all the application areas of Deep Learning in the entertainment industry.
Deep Learning Applications in Manufacturing
Smart factories are equipped with manufacturing intelligence that opens up all kinds of possibilities for data access from sensors containing multiple structures, formats, and semantics. As the leading innovation, Deep Learning generated a lot of attention in computational intelligence.
Deep Learning facilitates different levels of Data Analytics:
- Predictive analytics forecasts future production and equipment degradation.
- Descriptive analytics interprets historical data and analyzes operational parameters, conditions of the product, and environment to understand changes.
- Prescriptive analytics provides various scenarios to efficiently decide the course of action.
- Diagnostic analytics reports reasons for failure in equipment.
These advanced insights through Deep Learning can drive high performance in smart facilities with reduced downtime and operational costs, adjustments to customer demands, enhanced productivity, better visibility, and generally more value from operations.
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Deep Learning Applications in Fraud Detection
With the financial and banking sector going digital, fraud detection has become an added task. Deep Learning is aiding this sector through pattern identification in transactions and credit scores. This enables fraud prevention and detection by highlighting unusual behavior. Fraud detection algorithms are very useful to prevent fraud.
Autoencoders in TensorFlow and Keras are built to identify credit card frauds, thereby saving large amounts of money.While Machine Learning mostly draws attention to the cases of fraud that demand human deliberation, Deep Learning is trying to minimize these efforts.
Deep Learning examples in fraud detection: Development of risk profiles, e-commerce, payment fraud prevention, identity theft solutions, fraud detection in e-commerce, email risk scoring, etc.
Deep Learning Applications in Biomedicine
Deep Learning’s ability to integrate enormous datasets, apply existing knowledge, and learn arbitrarily complex relationships have made it ideal for biomedical research and applications. Through Deep Learning, it is already possible to predict the changes in cellular processes due to genetic variation, whether radiographic images indicate diseases, and to identify the molecules that will modulate the activity of therapeutically relevant proteins. However, significant research in this field is essential to fully realize the potential of Deep Learning.
Examples of Deep Learning in biomedicine: Genomic sequence analysis, medical image classification, protein structure classification and prediction, etc.
Deep Learning Applications in Image Processing
Deep Learning implements neural networks in image processing for picking up useful representations of features directly from data with no manual influence. Using training data, the network trains itself to recognize an object’s specific features. The network works with various layers, and every layer takes data from the previous one. With each passing layer, the complexity and detail of learning increases.
Examples of Deep Learning in image processing: Noise removal from images, image classification, image restoration, etc.
Interested in image processing, have a look at the Intellipaat OpenCV Tutorial.
Deep Learning Applications in Personalization’s
Providing customers with a personalized experience is now gaining traction, and giants such as Amazon, Netflix, and other e-commerce sites are trying to provide a seamless experience to their users in the form of personalized packages, recommendations, and opportunities around festive seasons. Pleasing the human psyche leads to growth in the market.
Deep Learning-enabled architectures allow for more refined models that can better predict the next set of actions that will be taken by a user. Then, they can provide further services accordingly.
Examples of Deep Learning in personalization: Chatbots, online self-service solutions, specialized robots, etc.
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Deep Learning Applications in Automatic Machine Translations
Automatic machine translation has been around for a long time, but Deep Learning has upped the game in:
- Automatic translation of the text
- Automatic translation of images
Text translations happen without any preprocessing of the sequence, which allows the algorithm to learn the dependencies between words and translate the text into a new language.
Convolutional neural networks can enable image identification that has visible letters. In instant visual translation, this image can be turned into text and then translated to recreate the image.
Examples of Deep Learning in automatic machine translations: TextGrabber, Google Translate, Papago, etc.
Deep Learning comes with endless possibilities and a mountain of opportunities for professionals with ample expertise. Now is the perfect time to upskill looking at real-world and future applications of deep learning!
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