From 8dfb633f24e5e5982625ceef8d0ee565cbcab3c7 Mon Sep 17 00:00:00 2001 From: julamarkova-vibe Date: Tue, 4 Aug 2026 10:01:22 +0200 Subject: [PATCH] Fix dead links Co-Authored-By: Claude Fable 5 --- 100DaysOfMLCode/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/100DaysOfMLCode/README.md b/100DaysOfMLCode/README.md index 7ad33c8..ce0c884 100644 --- a/100DaysOfMLCode/README.md +++ b/100DaysOfMLCode/README.md @@ -64,7 +64,7 @@ by reading its preface that will help to hone expertise on algorithms further as relevant papers which can be found as item **4** [here](https://github.com/aurimas13/Machine-Learning-Goodness) under Worthy Repositories while to get a good grasp of Category Theory started reading the book of Category Theory for Programmers by Bartosz Milewski [here](https://www.scribd.com/document/387084492/Category-Theory-for-Programmers) as also it can be inspected through [here](https://github.com/aurimas13/Machine-Learning-Goodness) under Worthy Books as item **3** where a blog on which the book is based is given. -- Dived into ML experiment tracking [here](https://neptune.ai/blog/ml-experiment-tracking) that in a few words is the process of saving all the experiment related information. +- Dived into ML experiment tracking [here](https://web.archive.org/web/20251127141526/https://neptune.ai/blog/ml-experiment-tracking) that in a few words is the process of saving all the experiment related information. ## Automated ML, Algorithm Design Manual & Logistic Regression | Day 9 @@ -605,7 +605,7 @@ To get either of the books for free if unavailable through links head and search - [Distill](https://distill.pub/) - The distinguishing trait of a Distill article is outstanding communication and a dedication to human understanding. Distill articles often, but not always, use interactive media. - [Google AI Blog](https://ai.googleblog.com/search/label/Machine%20Learning) - Google AI conducts research that advances the state-of-the-art in the field and the Google AI blog has a section specifically for machine learning research. - [Data Science Dojo](https://datasciencedojo.com/) - An education startup that enables professionals to extract actionable insights from data. They have an exciting machine learning blog, which has several very informative articles on machine learning. - - [Neptune.AI](https://neptune.ai/blog) - Remarkable machine learning blog, offering tutorials on machine learning modeling, hyperparameter optimization, model evaluation, data exploration, generative models, machine learning tools, and many more. Neptune.AI also offers a framework that makes it easier to track versions of your Jupyter notebooks, helps with managing your experimentation process, and integrates with your team’s workflow easily. + - [Neptune.AI](https://web.archive.org/web/20251130233447/https://neptune.ai/blog/) - Remarkable machine learning blog, offering tutorials on machine learning modeling, hyperparameter optimization, model evaluation, data exploration, generative models, machine learning tools, and many more. Neptune.AI also offers a framework that makes it easier to track versions of your Jupyter notebooks, helps with managing your experimentation process, and integrates with your team’s workflow easily. - [BAIR Berkeley](https://bair.berkeley.edu/blog/) - The BAIR blog provides an accessible, general-audience medium for researchers to communicate research findings, perspectives on the field, and various updates. Posts are from students, postdocs, and faculty in BAIR and intend to provide a relevant and timely discussion of research findings and results, both to experts and the general audience. - [OpenAI](https://openai.com/blog) - The OpenAI blog brings state-of-the-art research in the field. Their mission is to discover and enact the path to safe artificial general intelligence (AGI). - [Machine Learning (Theory) by John Langford](https://hunch.net/) - The Machine Learning (Theory) blog is an experiment in the application of a blog to academic research in machine learning and learning theory by machine learning researcher John Langford. He has emphasized that the field of machine learning “is shifting from an academic discipline to an industrial tool”.