Xudripo

Neural networks that predict what markets will do next

Explore the curriculum
Deep learning computational framework visualization

Markets move faster than most models can follow

Traditional forecasting breaks down when volatility spikes or patterns shift overnight. You need architectures that adapt in real time, trained on sequences that capture what actually drives price movement.

Our program builds recurrent and transformer-based systems that learn from temporal dependencies. You work with live data streams, backtesting frameworks, and deployment pipelines used by quantitative teams.

Every model you build processes actual market feeds. Every forecast you generate gets evaluated against next-day outcomes, so you see immediately what works and what fails.

Learning outcomes measured in model performance

18
Weeks of structured training
6
Live forecasting projects
34
Architecture implementations

What you actually build during the program

Recurrent network architecture implementation

Recurrent architectures for time series

Attention-based forecasting model

Attention layers for multi-step predictions

Choose how you learn best

Group sessions

Work alongside other learners in scheduled cohorts. Share debugging strategies, compare model performance, and iterate together through weekly challenges.

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Individual coaching

Direct access to instructors with flexible timing. Focus on specific architectures or datasets relevant to your work, with personalized feedback on every model you train.

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What past participants built after completing the program

I went from debugging shape mismatches to deploying a GRU model that runs inference on streaming quotes. The hands-on structure made every concept stick.

Nirmala Venkatesh

The private coaching let me focus entirely on volatility forecasting for options. I built three different architectures and learned exactly where each one breaks down.

Dmitri Ivanov

Group sessions pushed me to experiment faster. Seeing how others approached the same dataset gave me ideas I never would have tried alone.

Leilani Ocampo

Start building forecasting systems that actually work

No theoretical lectures. No toy datasets. Just architectures, real market feeds, and the engineering discipline required to ship models that handle live data without breaking.

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