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Gradient descent has a fundamental limitation: on most real-world loss surfaces, it is inefficient. When the surface has uneven curvature—steep in one direction and flat in another, which is common in practice—the algorithm struggles to make consistent progress. A high learning rate helps move faster along the flat direction but causes overshooting and oscillations along […]
Why Gradient Descent Zigzags and How Momentum Fixes It Read More »
In this tutorial, we walk through a complete, end-to-end workflow for correcting bias in survey data using the balance library. We simulate a realistic population, deliberately introduce sampling bias, and then apply multiple re-weighting techniques to recover unbiased estimates. We focus on four widely used methods: Inverse Probability Weighting (IPW), Covariate Balancing Propensity Scores (CBPS),
Researchers at Meta’s FAIR lab have released NeuralSet, a Python framework designed to eliminate one of the most persistent bottlenecks in Neuro-AI research: the painful, fragmented process of getting brain data into a deep learning pipeline. https://kingjr.github.io/files/neuralset.pdf The Problem: Neuroscience Data Is Stuck in the Pre-Deep-Learning Era Neuroscience already has excellent, battle-tested software. Tools like
In this tutorial, we explore how to use the ParseBench dataset to evaluate document parsing systems in a structured, practical way. We begin by loading the dataset directly from Hugging Face, inspecting its multiple dimensions, such as text, tables, charts, and layout, and transforming it into a unified dataframe for deeper analysis. As we progress,
LoRA is widely used for fine-tuning large models because it’s efficient, but it quietly assumes that all updates to a model are similar. In reality, they’re not. When you fine-tune for style (like tone, format, or persona), the changes are simple and concentrated in just a few dimensions — which LoRA handles well with low-rank
The LoRA Assumption That Breaks in Production Read More »
In this tutorial, we explore how we use BudouX to bring intelligent, phrase-aware line breaking to languages where whitespace is not naturally present, such as Japanese, Chinese, and Thai. We begin by setting up the library and working with its default parsers to understand how raw text is segmented into meaningful chunks. We then move
In this tutorial, we explore Datashader, a powerful, high-performance visualization library for rendering massive datasets that quickly overwhelm traditional plotting tools. We work through its full rendering pipeline in Google Colab, starting from dense point clouds and reduction-based aggregations to categorical rendering, line visualizations, raster data, quadmesh grids, compositing, and dashboard-style analytical views. As we
Tabular data—structured information stored in rows and columns—is at the heart of most real-world machine learning problems, from healthcare records to financial transactions. Over the years, models based on decision trees, such as Random Forest, XGBoost, and CatBoost, have become the default choice for these tasks. Their strength lies in handling mixed data types, capturing
In this tutorial, we build a comprehensive, hands-on understanding of DuckDB-Python by working through its features directly in code on Colab. We start with the fundamentals of connection management and data generation, then move into real analytical workflows, including querying Pandas, Polars, and Arrow objects without manual loading, transforming results across multiple formats, and writing
Optimice y modernice todo el proceso de planificación financiera con analítica El problema El ritmo de los cambios normativos, junto con el crecimiento explosivo de los datos financieros y operativos disponibles, ha hecho que las soluciones de contabilidad tradicionales sean incapaces de proporcionar la información crítica sobre los costes, la […] The post Conozca el
Conozca el coste real de su negocio Read More »
