Innovative AI solutions from research to production. Exploring the intersection of machine learning, finance, and practical applications.
Production-ready PPO implementation training AI to play Super Mario Bros. Features custom CNN architecture (4-layer with 512-unit FC), vectorized training with 8 parallel environments, and comprehensive evaluation tools. Achieves 80% success rate with 10M timesteps. Includes Docker support, TensorBoard/W&B integration, and modular codebase.
ML system forecasting F1 World Championship outcomes using official telemetry data. Combines Bayesian variance decomposition, 10,000+ Monte Carlo simulations, and ensemble ML (Random Forest, Gradient Boosting, SVM). Features 50+ engineered features, track classification, and hybrid predictions with confidence intervals.
Quantitative portfolio optimization platform combining AI-powered stock analysis (6-factor evaluation) with multi-strategy optimization. Implements Mean-Variance, Black-Litterman with AI views, Risk Parity, HRP, and CVaR optimization. Features macro regime analysis and institutional-grade constraints.
Production AI-powered diet generator with weekly meal plans (35 meals/week), AI recipe generation, and smart shopping lists. Live at diet.silviobaratto.com with Google authentication and subscription system. Complete full-stack solution with Docker deployment.
Advanced Conditional VAE generating 64×64 Pokemon images from text using dual conditioning: CLIP ViT-L/14 (768-dim) + categorical attributes (type, color, shape). Features BAML metadata pipeline with OpenAI vision, LPIPS perceptual loss, and 17.8M parameters. Production-ready with FastAPI + Angular frontend.
Python machine learning library for stock market analysis implementing LSTM, BiLSTM, GRU, BiGRU, MLP, and Bayesian Neural Networks. Includes probabilistic models (NNHMM, DMM) for financial time series with scikit-learn-style interfaces. Built on PyTorch and Pyro with S&P 500 data downloader.
Conditional VAE generating handwritten digit images (0-9) from natural language prompts. Uses SentenceTransformer for semantic text encoding (384-dim) with 20-dimensional latent space. Demonstrates text-conditioned generative modeling fundamentals with KL annealing.
Lightweight text prediction system that learns from WhatsApp conversations to suggest words as you type. Pure Python implementation with zero dependencies, achieving ~100k words/second training speed. Features real-time terminal UI and privacy-focused local processing.
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