Neural networks & marchine learning can be used to model non-linear, ambiguous or massive phenomena. I design customized architectures according to the nature of the data and business objectives, combining performance, accuracy and scalability.
Using CNN for image classification, object detection and semantic segmentation.
RNN and LSTM for time series analysis, forecasting and long dependency modeling.
Use of Machine Learning libraries such as Sckit-Learn and specific algorithms (Gradient booster, Random Forest, etc.)
Transformers for complex NLP tasks, BERT-like models for classification and information extraction.
GNN (Graph Neural Networks) for graphs: social networks, molecules, recommendation systems.
Creation of customized layers and specific loss functions to meet specific business requirements.
Creation of intelligent architectures, APIs, conversational agents, recommendation systems
Integration of models such as GPT, LLaMA, Mistral, Claude, etc. into business workflows
Adaptation of pre-trained models to specific corpora, supervised or reinforcement training
Design and training of deep learning models (CNN, RNN, Transformers) & machine learning (Random Forest, Scikit-Learn) for complex cases
Combining documentary research and generation for precise, contextualized answers
Design and deployment of modular, secure local AI architectures capable of processing data directly on the device
Containerization, CI/CD, monitoring, scalability, model security in production
Consultant specializing in the development of solutions based on theartificial intelligencethe language models (LLM) and neural networks.
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