A field guide for ML scientists moving into genomics and transcriptomics: why DNA only looks like text, why the whole species is one near-duplicate corpus, how regulation defeats the context window, the biology you cannot skip, why the molecule you sequence is not the one that acts, and what the famous foundation models do and don’t solve.
A field guide for ML scientists moving into radiology: what is genuinely easier than natural images, where computer-vision intuitions misfire, the data and labels you can actually get, how the FDA regulates these models, and why the model in the paper is rarely the one that ships.
A tour of the stack behind joseph-rich.com — Vercel, Cloudflare, Jekyll, giscus, Jupyter/Colab, conda + Docker, pytest + GitHub Actions, a pre-commit publish hook, and Markdown-with-LaTeX — and the one principle tying them together: a blog post should be as reproducible as the experiment it describes.
A field guide for ML scientists moving into genomics and transcriptomics: why DNA only looks like text, why the whole species is one near-duplicate corpus, how regulation defeats the context window, the biology you cannot skip, why the molecule you sequence is not the one that acts, and what the famous foundation models do and don’t solve.
A tour of the stack behind joseph-rich.com — Vercel, Cloudflare, Jekyll, giscus, Jupyter/Colab, conda + Docker, pytest + GitHub Actions, a pre-commit publish hook, and Markdown-with-LaTeX — and the one principle tying them together: a blog post should be as reproducible as the experiment it describes.
A field guide for ML scientists moving into genomics and transcriptomics: why DNA only looks like text, why the whole species is one near-duplicate corpus, how regulation defeats the context window, the biology you cannot skip, why the molecule you sequence is not the one that acts, and what the famous foundation models do and don’t solve.
A field guide for ML scientists moving into radiology: what is genuinely easier than natural images, where computer-vision intuitions misfire, the data and labels you can actually get, how the FDA regulates these models, and why the model in the paper is rarely the one that ships.
A field guide for ML scientists moving into radiology: what is genuinely easier than natural images, where computer-vision intuitions misfire, the data and labels you can actually get, how the FDA regulates these models, and why the model in the paper is rarely the one that ships.
A field guide for ML scientists moving into genomics and transcriptomics: why DNA only looks like text, why the whole species is one near-duplicate corpus, how regulation defeats the context window, the biology you cannot skip, why the molecule you sequence is not the one that acts, and what the famous foundation models do and don’t solve.
A tour of the stack behind joseph-rich.com — Vercel, Cloudflare, Jekyll, giscus, Jupyter/Colab, conda + Docker, pytest + GitHub Actions, a pre-commit publish hook, and Markdown-with-LaTeX — and the one principle tying them together: a blog post should be as reproducible as the experiment it describes.
A field guide for ML scientists moving into radiology: what is genuinely easier than natural images, where computer-vision intuitions misfire, the data and labels you can actually get, how the FDA regulates these models, and why the model in the paper is rarely the one that ships.
A tour of the stack behind joseph-rich.com — Vercel, Cloudflare, Jekyll, giscus, Jupyter/Colab, conda + Docker, pytest + GitHub Actions, a pre-commit publish hook, and Markdown-with-LaTeX — and the one principle tying them together: a blog post should be as reproducible as the experiment it describes.
A tour of the stack behind joseph-rich.com — Vercel, Cloudflare, Jekyll, giscus, Jupyter/Colab, conda + Docker, pytest + GitHub Actions, a pre-commit publish hook, and Markdown-with-LaTeX — and the one principle tying them together: a blog post should be as reproducible as the experiment it describes.
A field guide for ML scientists moving into genomics and transcriptomics: why DNA only looks like text, why the whole species is one near-duplicate corpus, how regulation defeats the context window, the biology you cannot skip, why the molecule you sequence is not the one that acts, and what the famous foundation models do and don’t solve.