Machine Learning & Deep Learning - From analysis to innovation

Real data is often non-linear, ambiguous and massive. The machine learning and the deep learning transform this complexity into reliable predictions, intelligent automation and sustainable competitive advantages.

Computer vision : image classification, object detection and segmentation to automate visual analysis and accelerate decision-making.
Sequential data : time series modeling and forecasting with RNN and LSTM, to anticipate trends, behaviors or anomalies.
Classic machine learning : proven algorithms (Random Forest, Gradient Boosting, etc.) for cases where explicability and robustness are essential.
Natural language processing : Transformers and BERT-like models to extract meaning, classify documents and automate text analysis.
Relational data : Graph Neural Networks to reveal hidden relationships in social networks, molecules or recommendation systems.
Advanced customization : tailor-made architectures and loss functions to meet specific business requirements with precision.

Objective: transform data complexity into actionable intelligence, creating models capable of learning, adapting and generating a measurable impact on performance and strategy.

What's the difference between machine learning and deep learning?

Visit Machine Learning is a branch of artificial intelligence that involves training algorithms to learn from data and improve their performance without being explicitly programmed; it relies on statistical models capable of detecting patterns and making predictions.

Visit Deep Learningis a sub-field of Machine Learning that uses multi-layer artificial neural networks (known as "deep" neural networks) to process massive volumes of data and automatically extract complex representations.

In short: Machine Learning learns from structured data with models that are often simpler and easier to interpret, while Deep Learning excels at analyzing unstructured data (images, sounds, text) thanks to its neural-inspired modeling power (Artificial neural network).