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hamaadshah/README.md

Hamaad Shah

  • British citizen with 15+ years experience and expertise in quantitative analytics, machine learning, data science and data engineering - applicable to the banking, insurance and consulting domains.
  • Extensive expertise in deep learning, Bayesian inference, Natural Language Processing (NLP), Computer Vision (CV), etc., applied to various use cases such as Asset Liability Management (ALM), actuarial pricing, trader surveillance, anti-financial crime, etc..
  • MSc in Applicable Mathematics from the London School of Economics and Political Science (LSE), UK.
  • BSc in Economics (majored in Econometrics and Mathematical Economics) from the University of Manchester, UK.
  • Invited guest lecturer at the University of Oxford Department of Continuing Education (DCE) course "Artificial Intelligence - Cloud and Edge Implementations".
  • CFA levels 1 and 2 exams passed on first attempts with CFA level 3 exam to be completed in due course.
  • Please note that the below stats are related to my personal efforts as I cannot publish my professional efforts to GitHub, etc., for obvious reasons.

Reference for GitHub stats card: https://github.com/anuraghazra/github-readme-stats

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  1. market_risk_gan_tensorflow market_risk_gan_tensorflow Public

    Using Bidirectional Generative Adversarial Networks to estimate Value-at-Risk for Market Risk Management using TensorFlow.

    Python 86 43

  2. autoencoders_tensorflow autoencoders_tensorflow Public

    Automatic feature engineering using deep learning and Bayesian inference using TensorFlow.

    Python 72 37

  3. gan_tensorflow gan_tensorflow Public

    Automatic feature engineering using Generative Adversarial Networks using TensorFlow.

    Python 51 28

  4. gan_deeplearning4j gan_deeplearning4j Public

    Automatic feature engineering using Generative Adversarial Networks using Deeplearning4j and Apache Spark.

    Java 21 8

  5. autoencoders_pytorch autoencoders_pytorch Public

    Automatic feature engineering using deep learning and Bayesian inference using PyTorch.

    Python 20 7

  6. fair_ml_R fair_ml_R Public

    Creating fair machine learning models with Generative Adversarial Networks using R.

    R 5 2