Data Science

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A Coding Implementation to Master GPU Computing with CuPy, Custom CUDA Kernels, Streams, Sparse Matrices, and Profiling

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In this tutorial, we delve into CuPy as a powerful GPU-accelerated alternative to NumPy for high-performance numerical computing in Python. We start by inspecting the available CUDA device, checking the CuPy version, runtime details, GPU memory, and compute capability so that we understand the hardware environment before running heavy computations. Then, we compare NumPy and […]

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A Coding Implementation to Portfolio Optimization with skfolio for Building Testing, Tuning, and Comparing Modern Investment Strategies

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In this tutorial, we explore skfolio, a scikit-learn compatible portfolio optimization library that helps us build, compare, and evaluate different investment strategies in a structured Python workflow. We start by loading S&P 500 price data, converting it into returns, and creating a time-based train-test split suitable for financial analysis. From there, we build simple baseline

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How to Build Technical Analysis and Backtesting Workflow with pandas-ta-classic, Strategy Signals, and Performance Metrics

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In this tutorial, we implement how to use pandas-ta-classic to build a complete technical analysis and trading strategy workflow. We start by installing the required libraries, downloading historical OHLCV stock data with yfinance, cleaning the returned data structure, and inspecting the available indicator categories inside the library. We then calculate popular indicators such as SMA,

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How to Build a Single-Cell RNA-seq Analysis Pipeline with Scanpy for PBMC Clustering, Annotation, and Trajectory Discovery

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In this tutorial, we perform an advanced single-cell RNA-seq analysis workflow using Scanpy on the PBMC-3k benchmark dataset. We start by loading the dataset, inspecting its structure, and applying quality control checks to evaluate gene counts, total counts, mitochondrial content, and ribosomal gene signals. We then filter low-quality cells and genes, detect potential doublets with

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Meta FAIR Releases NeuralSet: A Python Package for Neuro-AI That Supports fMRI, M/EEG, Spikes, and HuggingFace Embeddings

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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

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A Coding Implementation on Document Parsing Benchmarking with LlamaIndex ParseBench Using Python, Hugging Face, and Evaluation Metrics

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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,

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The LoRA Assumption That Breaks in Production 

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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

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How to Build Smarter Multilingual Text Wrapping with BudouX Through Parsing, HTML Rendering, Model Introspection, and Toy Training

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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

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A Coding Tutorial on Datashader on Rendering Massive Datasets with High-Performance Python Visual Analytics

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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

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