Deep learning is a machine learning technique using artificial neural networks. When trained on large, high-quality datasets, it achieves high accuracy. This makes it useful in areas with abundant data where accurate predictions are valuable.
Please see deep learning capabilities and its applications based on industry and function.
What are the capabilities & technologies enabled by deep learning?
A deep learning model can identify, classify, and analyze structured data, images, text, or sound.
Computer Vision
Computer vision includes comprehending a visual environment and its context. For forming a computer vision model, there are three steps:
- acquiring an image from data sets
- processing the image automatically with deep learning algorithms
- identifying the image and its class.
The types of computer vision include image classification & segmentation and object detection & tracking.
Image recognition and segmentation
Deep learning models can discriminate an image from others and classify it by using predefined and labeled categories. Convolutional neural networks (CNN) are deep learning networks and are mostly used in this domain. To ease the analysis of the image, image segmentation models are used.
Today, image recognition and segmentation algorithms are used in different areas, from our daily activities to future technologies. For instance, this technology allows us to
- analyze medical images more accurately
- develop self-driving cars
- do fingerprint, iris, and face matching for biometric processes
- look up details for artwork
- have smarter home security systems
Object detection and tracking
An image contains various objects and object detection algorithms are applied for localisation and classification of these objects. Object detection models build bounding boxes around objects and determine the objects within the bounding box. Object tracking can be implemented after the detection of the object.
When an object moves in the bounding box, object tracking models track this object into the next images and update the bounding boxes. These models are used for
- face recognition from images
- identification of a specific individual in the photos/images
You can read more about our object detection benchmark.
Natural language processing (NLP)
Natural language processing algorithms interpret and analyze natural language data in textual or verbal forms. It enables generating human language and speech or identifying the speaker based on differences in voice.
NLP deep learning applications include speech recognition, text classification, sentiment analysis, text simplification and summarisation, writing style recognition, machine translation, parts-of-speech tagging, and text-to-speech tasks. This technology helps us for
- virtual voice/smart assistants
- Digital workers
- e-mail filters
- autocorrect & autocomplete text checks
- communicating with chatbots
- translating languages in real-time
Feel free to check our related research about NLP.
Automated predictions
As we mentioned in our deep learning software guide, deep learning models can provide better, faster, cheaper, and more valuable predictions than other machine learning approaches. This is especially true in cases where a large volume of high-quality training data is available.
Predictive models based on deep artificial neural networks (i.e., deep learning) can work with vast amounts of data, realize nonlinear relationships, and figure out complex patterns.
What are deep learning use cases in different industries and sectors?
Agriculture
- Boost crop yields and optimize resource use by leveraging sensor data and satellite imagery through advanced deep-learning techniques. Drawing on precision agriculture principles, an Agro Deep Learning Framework (ADLF) can analyze environmental factors like temperature and humidity to improve decision-making and proactively address potential crop issues.1
Aerospace & Defence
- Deep learning, particularly CNNs and vision transformers, offers a more effective way to identify objects from complex, high-resolution satellite imagery, overcoming limitations of traditional methods.2 Models like ResNet and EfficientNet have shown promising classification results.
- Automate the detection of suspicious events using AI-powered surveillance systems. Deep learning algorithms analyze video feeds to identify anomalies and unusual human behaviors, triggering alerts when potential threats are recognized. This approach moves beyond simple event recording to proactive threat identification.3
Automotive
- Develop autonomous things including vehicles. There are numerous deep learning models used in such devices including those for detecting traffic signs & lights, other vehicles, pedestrians, etc.
Financial services
- Estimate future stock market prices
- Detect fraudulent activities with higher accuracy and fewer false positives
- Evaluate a client’s creditworthiness by analyzing information from multiple sources and responding to loan applications faster
- Identify the next best actions for each customer
- Develop and refine automated trading strategies using deep reinforcement learning models that adapt to market fluctuations and maximize profitability.
- Automate Know Your Customer (KYC) and Anti-Money Laundering (AML) checks by leveraging deep learning to analyze customer data, identify suspicious patterns, and reduce false positives, improving regulatory compliance.
Feel free to read our article on deep learning use cases in finance for more.
Healthcare
- Diagnose diseases leveraging medical imaging solutions, for example, recognition of potential cancerous lesions on radiology images
- Personalize medical treatments
- Determine patients most at risk in the healthcare system
Feel free to read our article on deep learning use cases in healthcare for more.
Insurance
- Automate claims and damage analysis process by utilizing deep learning to analyze incoming reports or images, significantly reducing manual effort and speeding up resolution times.
- Enhance the accuracy of risk prediction for home insurance by deploying image-based deep learning models that can identify potential hazards and vulnerabilities in property images.
- Implement AI-powered models to improve pricing risk, leveraging a broader range of data points and advanced algorithms to generate more precise and competitive insurance premiums.
Manufacturing
Manufacturing companies, including discrete manufacturing like automotive or other industrial companies (e.g. oil&gas), rely on deep learning algorithms:
- Provide advanced analytics tools for processing big data about manufacturing
- Generate automated alerts about the issues of production lines (e.g. on quality assurance or safety) using sensor data to notify relevant teams on time
- Support predictive maintenance systems by analyzing images and other sensor data
- Empower industrial robots with sensors and computer vision skills
- Monitor the working environment around heavy machineries automatically to ensure people and items are at a safe distance
Feel free to read our article on deep learning manufacturing use cases for more.
Pharmaceuticals & Medical Products
- Predict drug effects, monitor drug use, and identify side effects with deep learning models that can analyze vast amounts of pharmaceutical data to accelerate drug discovery and development.
- Enable precision medicine by leveraging patient data from various sources to predict patient treatment, which includes remedies based on genetic, environmental, or lifestyle factors (also called personalized medicine).
- Schedule the maintenance time of medical equipment by using sensor data to predict possible malfunctions.
- Accelerate clinical trial analysis for faster drug approval with automated systems that can efficiently process and interpret clinical trial data, helping to speed up the regulatory approval process.
- Visualize rare disease diagnosis through enhanced medical imaging, leveraging advanced image processing techniques powered by AI to improve detecting and identifying subtle indicators.
- Predict real-time disease outbreaks using epidemiological data with AI and forecasting the spread of infectious diseases like influenza to inform public health interventions and mitigate the impact of outbreaks.
Public sector
- Employ advanced analytics and machine learning to predict population health risks, allowing public health officials to proactively allocate resources and implement targeted interventions to improve community well-being.
- Enhance security protocols through facial recognition for security checks, enabling faster and more accurate identification of individuals in public spaces and at government facilities.
- Implement deep learning models to analyze crime data and identify high-risk areas or individuals, allowing law enforcement to allocate resources and prevent criminal activity proactively.
Retail & E-commerce
- Offer new shopping experiences such as “Just Walk Out” stores and checkout-less shopping. For more, feel free to read our article on cashierless stores.
- Other shopping experiences powered by deep learning include voice-enabled shopping and in-store robots.
- Image search: Scanning the image of the product to find the product in the store or suggest similar alternatives
- Forecasting product demand more accurately according to buying habits analysis and future trend predictions
- Deliver effective inventory management to prevent out-of-stock and oversupply
- Provide personalized shopper experience based on browsing/purchasing history in-store or online
- Formulate personalized recommendations and reminders, such as style matches for fashionistas
- Implement dynamic pricing strategies using deep learning algorithms that analyze real-time market conditions, competitor pricing, and demand patterns to optimize pricing for increased revenue.
What are deep learning use cases in different departments or functions?
Analytics
- Most deep learning applications empower analytics solutions. Therefore analytics departments rely on deep learning in numerous cases
Customer success
- Chatbots offering immediate and personalized customer service
- Monitor customers’ responses, reviews, and social media activity to identify what they say about the brand
- Churn prevention: Examine data in customer feedback forms/texts, identify potential churners, and communicate with the customer without losing time
Cybersecurity
- Intrusion detection/prevention systems (IDS / IPS): Investigate user activities and network traffic to prevent malicious activities and reduce false alerts
Operations
- Automatically extract data from documents using deep learning models and OCR (Optical Character Recognition) technology, transforming unstructured information from scanned images and PDFs into readily accessible and usable digital formats, streamlining data entry and reducing manual processing time.
You can check our OCR benchmark to see the accuracy of the various OCR tools for different document types.
Sales & Marketing
- Leverage machine learning algorithms to create personalized advertisements according to browsing data, maximizing engagement by delivering targeted content that resonates with individual user interests and preferences.
- Employ predictive analytics powered by AI to identify potential clients who are most likely to buy the solution, enabling sales teams to prioritize leads and focus their efforts on high-potential prospects, improving conversion rates.
- Utilize deep learning models for logo and counterfeit item detection in social media for brand protection, proactively identifying and flagging unauthorized use of brand assets and enabling swift action to mitigate reputational damage and prevent revenue loss.
Supply Chain
- Optimize routing to reduce costs, carbon footprint, and delivery times
- Identify driver or vehicle performance improvement suggestions based on sensor data
- Improve the accuracy of demand forecasting by leveraging deep learning to analyze historical sales data, external economic factors, and social media trends, optimizing inventory levels and reducing stockouts or overstocking.
If you are ready to use deep learning in your firm, we prepared a data driven list of companies offering deep learning platforms.
If you need help in choosing among deep learning vendors who can help you get started, let us know:
External Links
- 1. Improving crop production using an agro-deep learning framework in precision agriculture | BMC Bioinformatics | Full Text. BioMed Central
- 2. Review of deep learning methods for remote sensing satellite images classification: experimental survey and comparative analysis | Journal of Big Data | Full Text. Springer International Publishing
- 3. ResearchGate - Temporarily Unavailable.
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