A big reason I started this blog (almost twenty years ago!) was to have a safe space to rant about things I’m obsessed with. One of those obsessions is TV, but growing up in the UK and living in the US most of my adult life has left me with tastes that don’t seem to match up with anyone’s demographic. That means I spend a lot of time trying to find shows that I enjoy, and while I […]
OpenAI team has released their openai/circuit-sparsity model on Hugging Face and the openai/circuit_sparsity toolkit on GitHub. The release packages the models and circuits from the paper ‘Weight-sparse transformers have interpretable circuits‘. https://arxiv.org/pdf/2511.13653 What is a weight sparse transformer? The models are GPT-2 style decoder only transformers trained on Python code. Sparsity is not added after training, it is enforced during optimization. After each AdamW step, the training loop keeps only the largest magnitude entries in every weight matrix […]
Get started with five free Python libraries that let you analyze, filter, and process data faster than traditional Pandas.
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Writing efficient Python data classes cuts boilerplate while keeping your code clean. And this article will teach you how.
Introduction If you work in data science, data engineering, or as as a frontend/backend developer, you deal with JSON. For professionals, its basically only death, taxes, and JSON-parsing that is inevitable. The issue is that parsing JSON is often a serious pain. Whether you are pulling data from a REST API, parsing logs, or reading […] The post Stop Writing Spaghetti if-else Chains: Parsing JSON with Python’s match-case appeared first on Towards Data Science.
In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science and AI, their writing, and their sources of inspiration. Today, we’re thrilled to share our conversation with Maria Mouschoutzi. Maria is a Data Analyst and Project Manager with a strong background in Operations Research, Mechanical […] The post The Skills That Bridge Technical Work and Business Impact appeared first on Towards Data Science.
Softmax Regression is simply Logistic Regression extended to multiple classes. By computing one linear score per class and normalizing them with Softmax, we obtain multiclass probabilities without changing the core logic. The loss, the gradients, and the optimization remain the same. Only the number of parallel scores increases. Implemented in Excel, the model becomes transparent: you can see the scores, the probabilities, and how the coefficients evolve over time. The post The Machine Learning “Advent Calendar” Day 14: […]
Can a 3B model deliver 30B class reasoning by fixing the training recipe instead of scaling parameters? Nanbeige LLM Lab at Boss Zhipin has released Nanbeige4-3B, a 3B parameter small language model family trained with an unusually heavy emphasis on data quality, curriculum scheduling, distillation, and reinforcement learning. The research team ships 2 primary checkpoints, Nanbeige4-3B-Base and Nanbeige4-3B-Thinking, and evaluates the reasoning tuned model against Qwen3 checkpoints from 4B up to 32B parameters. https://arxiv.org/pdf/2512.06266 Benchmark results On AIME […]
Everyone talks about LLMs—but today’s AI ecosystem is far bigger than just language models. Behind the scenes, a whole family of specialized architectures is quietly transforming how machines see, plan, act, segment, represent concepts, and even run efficiently on small devices. Each of these models solves a different part of the intelligence puzzle, and together they’re shaping the next generation of AI systems. In this article, we’ll explore the five major players: Large Language Models (LLMs), Vision-Language Models […]
