Video2IIA in picturesThe school, our teaching and our programmes.Watch
Build and verify guided data and AI projects with an AI assistant. Describe the goal, have the tools prepare the solution and check the results with your trainer; memorising or writing syntax by hand is not the objective.
PyTorch & TensorFlow: gradients, optimisers (AdamW, RMSProp), data augmentation.
Convolutional networks (ResNet, EfficientNet), object detection (YOLO), semantic segmentation (U-Net).
Transformer models (BERT, RoBERTa), fine-tuning, Retrieval-Augmented Generation (RAG), conditional generation.
RNN/LSTM, attention mechanisms, Transformer architectures for forecasting.
Probabilistic diffusion (Stable Diffusion), GANs, audio models (MusicGen).
Multi-page apps, authentication, smart caching, vector database integration.
Data validation, rate limiting, asynchronous integration, security.
Containerisation, lightweight orchestration, serverless and managed platforms (Railway, Hugging Face).
Experiment tracking (Weights & Biases), model tracking (MLflow), production monitoring.
Retrieval over a private knowledge base with an interactive Streamlit interface.
A conditional generation system using latent diffusion models (fine-tuned Stable Diffusion).
Real time for financial, energy or medical data — Transformer-based forecasting.
For Telegram/Discord: contextual memory, image and text understanding.
From imaging: CNNs with explainability mechanisms (Grad-CAM, SHAP).
Ready to build your first AI application in production?