Deep Learning
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- Agent-Oriented Architecture
- Agentic AI Alignment
- Agentic AI for Customer Engagement
- Agentic AI for Decision Support
- Agentic AI for Knowledge Management
- Agentic AI for Predictive Operations
- Agentic AI for Process Optimization
- Agentic AI for Workflow Automation
- Agentic AI Safety
- Agentic AI Strategy
- Agile Development
- Agile Development Methodology
- AI Agents for IT Service Management
- AI for Compliance Monitoring
- AI for Demand Forecasting
- AI for Edge Computing (Edge AI)
- AI for Energy Consumption Optimization
- AI for Predictive Analytics
- AI for Predictive Maintenance
- AI for Real Time Risk Monitoring
- AI for Telecom Network Optimization
- AI Orchestration
- Algorithm
- API Integration
- API Management
- Application Modernization
- Applied & GenAI
- Artificial Intelligence
- Augmented Reality
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At Xebia, Deep Learning means using multi layer neural networks to automatically learn complex patterns from large amounts of data. Unlike traditional machine learning, deep learning models extract hierarchical features on their own, which makes them especially powerful for handling unstructured data such as images, text, audio, and video.
Xebia helps clients design, train, and deploy deep learning systems. This includes selecting architectures like CNNs, RNNs, and Transformers, building production pipelines, monitoring model drift, and scaling inference. The goal is to transform data into high impact intelligence with minimal manual feature engineering.
What Are the Key Benefits of Deep Learning?
- Ability to extract features automatically, reducing manual feature engineering
- Superior performance for complex tasks such as image classification, natural language understanding, and speech recognition
- Scalability as models improve with more data
- Flexibility to adapt to multiple data modalities (vision, text, audio)
- Potential for transfer learning: reusing pretrained models to accelerate new tasks
- Better generalization across varied inputs, thanks to deep representations
What Are Some Deep Learning Use Cases at Xebia?
- Sentiment classification in NLP (classifying text as positive/negative)
- Object detection & recognition in images and video streams
- Generative models for text, image, or code generation
- Time series forecasting and anomaly detection
- Recommendation systems using embeddings
- Autonomous or semi-autonomous systems relying on vision or sensor input
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