Top 50 Deep Learning Use Case & Case Studies
Deep learning uses artificial neural networks to learn from data. When trained on large, high-quality datasets, it achieves high accuracy, making it valuable wherever you have abundant data and need accurate predictions.
Below are real deep learning applications across industries and business functions, with concrete examples.
What are the capabilities & technologies enabled by deep learning?
Deep learning models identify, classify, and analyze structured data, images, text, and sound. Three main capabilities:
Computer Vision
Computer vision includes comprehending a visual environment and its context. The process:
- Acquire images from datasets
- Process images with deep learning algorithms
- Identify and classify what’s in the image
Image recognition and segmentation
Convolutional neural networks (CNNs) discriminate between images and classify them into predefined categories. Image segmentation breaks images into smaller parts for easier analysis.
Real applications:
- Medical imaging analysis (detecting tumors in X-rays and MRIs)
- Self-driving car development
- Biometric authentication (fingerprint, iris, face matching)
- Artwork identification and details lookup
- Smart home security systems
Object detection and tracking
Object detection algorithms find and classify multiple objects in images by drawing bounding boxes around them. Object tracking follows these objects across video frames.
Real applications:
- Face recognition in photos and video
- Identifying specific individuals in crowds
- Security surveillance systems
Natural language processing (NLP)
NLP algorithms interpret and analyze natural language in text or speech. This enables generating human language, recognizing speech, and identifying speakers by voice.
NLP applications:
- Speech recognition
- Text classification
- Sentiment analysis
- Text summarization
- Writing style recognition
- Machine translation
- Text-to-speech
Real uses:
- Virtual assistants (Alexa, Siri, Google Assistant)
- Digital workers handling customer inquiries
- Email spam filters
- Autocorrect and autocomplete
- Chatbots for customer service
- Real-time language translation
Automated predictions
Deep learning models provide better, faster, cheaper, and more accurate predictions than traditional machine learning, especially when you have large volumes of high-quality training data.
Deep artificial neural networks work with vast amounts of data, identify nonlinear relationships, and recognize complex patterns that simpler algorithms miss.
What are deep learning use cases in different industries and sectors?
Agriculture
- Agro Deep Learning Framework (ADLF) analyzes environmental factors like temperature, humidity, and soil moisture to improve decision-making and address potential crop issues before they become problems.1
Aerospace & Defence
- CNNs and vision transformers identify objects from complex, high-resolution satellite imagery, overcoming limitations of traditional methods.2 Models like ResNet and EfficientNet have shown promising classification results.
- Deep learning algorithms analyze video feeds to automatically detect suspicious events. The system identifies anomalies and unusual behaviors, triggering alerts when potential threats appear moving beyond simple 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
- Stock market price prediction
- Fraud detection with fewer false positives
- Credit risk assessment (analyzing multiple data sources)
- Customer next-best-action recommendations,
- Automated trading strategies using deep reinforcement learning
- 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
- Automated claims processing (analyzing reports and images to reduce manual effort)
- Risk prediction for home insurance (identifying hazards from property images)
- Pricing optimization (using broader data points for precise premiums) 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
- Drug effect prediction and side effect identification
- Precision medicine (personalized treatment based on genetics, environment, lifestyle)
- Medical equipment maintenance scheduling
- Clinical trial analysis acceleration
- Rare disease diagnosis visualization
- Real-time disease outbreak prediction
Public sector
- Population health risk prediction
- Facial recognition for security checks
- Crime data analysis to identify high-risk areas
Retail & E-commerce
- Checkout-less stores (“Just Walk Out” technology)
- Voice-enabled shopping
- In-store robots
- Image search (scan a product to find it or similar alternatives)
- Deliver effective inventory management to prevent out-of-stock and oversupply
- Demand forecasting from buying habits and trend analysis
- Inventory management preventing stockouts and oversupply
- Personalized shopping based on browsing/purchase history
What are deep learning use cases in different departments or functions?
Analytics
- Most deep learning applications power analytics solutions, so analytics departments rely on deep learning across numerous use cases.
Customer success
- Chatbots providing immediate, personalized service
- Social media and review monitoring to track brand sentiment
- Churn prevention (identifying potential churners from customer feedback and behavior)
Cybersecurity
- Intrusion detection/prevention systems (IDS / IPS): Investigate user activities and network traffic to prevent malicious activities and reduce false alerts
Operations
- Deep learning models combined with OCR (Optical Character Recognition) automatically extract data from scanned images and PDFs, transforming unstructured information into usable digital formats.
You can check our OCR benchmark to see the accuracy of the various OCR tools for different document types.
Sales & Marketing
- Personalized advertisements based on browsing data
- Lead scoring (identifying prospects most likely to buy)
- Logo and counterfeit detection on social media for brand protection
Supply Chain
- Route optimization to reduce costs, carbon footprint, and delivery times
- Driver/vehicle performance improvement from sensor data
- Demand forecasting (analyzing historical sales, economic factors, social media trends)
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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