GPU-Powered ML, AI, and Quant Finance with Google Colab (1)

GPU-Powered ML, Data & Quant Finance with Google Colab — Session 1 🚀 See how a free Google Colab T4 GPU (and a paid L4 GPU) can speed up your ML, data, and quant finance workflows by 10-100x — right in your browser. In this first session of our free 3-part webinar series, Dr. Yves J. Hilpisch walks through four ready-to-run notebooks that demonstrate dramatic GPU acceleration on common workloads. 📘 Notebooks in this session: 00 — Colab Basics: runtime info, pip install, git clone, Google Drive 01 — Hardware Accelerated ML: Random Forest, K-Means, matmul, MLP (CPU vs GPU) 02 — GPU DataFrames with cuDF: groupby, merge, rolling window on synthetic data 03 — GPU Monte Carlo for Finance: option pricing, barrier options, Greeks at GPU speed 04 — GPU Monte Carlo Calibration: fitting a Merton jump-diffusion model ⚡ Measured speed-ups on a free T4 GPU: • Random Forest: ~18x • K-Means: ~39x • Monte Carlo pricing: ~76x • Monte Carlo calibration: ~102x 🔗 Resources: 📑 Slides: https://tpq.io/tpq_colab_1.pdf 📂 Notebook repo: https://github.com/yhilpisch/colab 📈 CPF: https://cpf.tpq.io 🤖 TAE: https://theaiengineer.dev ₿ TCE: https://thecryptoengineer.dev 📊 TDS: https://thedatascientist.dev 📌 Disclaimer: This content is provided solely for illustration and educational purposes. The Python Quants GmbH is not affiliated with, endorsed by, or sponsored by Google. All numbers and specifications are indicative; always check the official Google Colab documentation for current and reliable information. #GoogleColab #GPU #MachineLearning #QuantFinance #MonteCarlo #cuDF #RAPIDS #Python #DataScience #AIEngineering