David Foster
Founding Partner at Applied Data Science Partners
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolDavid Foster is a founding partner of Applied Data Science Partners in London, bringing nine years of hands-on experience designing and deploying advanced data science and generative AI solutions for commercial clients. He authored O'Reilly’s Generative Deep Learning and maintains the book’s official codebase, contributing practical model training and hardware-optimization improvements (including M1 and GPU tooling) to real-world transformer examples. With an academic foundation in mathematics (Cambridge) and operational research (Warwick), he blends rigorous theory with product-focused delivery from consulting and senior data science roles. Known for translating research-grade generative models into production-ready workflows, he combines entrepreneurial leadership with deep technical craftsmanship.
9 years of coding experience
4 years of employment as a software developer
Master of Arts (MA) Mathematics, Master of Arts (MA) Mathematics at University of CambridgeMaster of Science (MSc) Management Science and Operational Research, Master of Science (MSc) Management Science and Operational Research at University of Warwick - Warwick Business SchoolPGCE Mathematics, PGCE Mathematics at University of Bedfordshire
Stackoverflow
806reputation
18kreached
2answers
2questions
Github Skills (12)
transformer10
jupyter-notebook10
machine-learning10
deep-learning10
tensorflow10
gpu10
python10
data-science9
boosting6
decision-tree6
xgboost6
r6
Programming languages (9)
Github contributions (5)
davidADSP/Generative_Deep_Learning_2nd_Edition
Feb 2023 - Feb 2023
The official code repository for the second edition of the O'Reilly book Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play.
Role in this project:
ML Engineer
ML EngineerContributions:51 commits, 3 PRs, 28 pushes in 18 days
Contributions summary:David's commits primarily focused on modifying a Jupyter Notebook, specifically within the context of an autoregressive transformer model for text generation. Their contributions included the addition of code related to model training, the incorporation of a TensorFlow-based model, and adjustments for hardware optimization (mac m1, GPU docker image, GPU machine). These changes suggest the user's involvement in the development, training, and optimization of a deep learning model, fitting within the scope of the Generative Deep Learning project.
chatgptdalle2data-sciencedeep-learningdiffusion-models
davidADSP/GDL_code
Feb 2019 - Feb 2023
The official code repository for examples in the O'Reilly book 'Generative Deep Learning'
Contributions:13 PRs, 41 pushes, 6 branches in 4 years
pytorchdeep-learningmachine-learningcode-repositorygenerative
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