Focuses on the latest large-model trends, applications, technical architectures, and related information.
This article walks through enabling Hyper‑V on Windows 11 Pro, configuring Docker Desktop with Chinese mirrors, adjusting storage, installing Ubuntu via WSL, cloning and setting up Dify, running Docker Compose, and linking Ollama's LLM so the AI agent runs entirely on a local machine.
Drawing from a recent AI hardware lecture, this article outlines the market opportunities, product definition frameworks, development processes, and the evolving role of product managers, illustrating concepts with real-world examples like AI companion toys and self‑distillation models.
The article breaks down artificial intelligence into four essential components—massive data, powerful compute, effective algorithms, and real‑world scenarios—explaining each element with concrete analogies, hardware benchmarks, algorithm classifications, and a list of typical AI applications.
This article explains how to replace manual print debugging with the lightweight PySnooper decorator, showing step‑by‑step installation, usage, log configuration, and detailed output examples that reveal line execution and variable changes in Python functions.
This article walks through the theory of k‑nearest‑neighbors, explains feature selection and normalization, shows how to implement the algorithm with plain JavaScript classes, visualizes results on an HTML canvas, and discusses practical limitations and extensions.
This article walks through a complete Python implementation of the CART decision‑tree algorithm on the Banknote dataset, covering data loading, cross‑validation splitting, Gini impurity calculation, recursive tree construction, prediction, and performance evaluation with concrete code examples.
This comprehensive guide walks through NumPy fundamentals—creating ndarrays, setting data types, performing vectorized arithmetic, indexing, slicing, boolean and fancy indexing, transposition, ufuncs, where, statistical functions, linear algebra, random generation, reshaping, and array splitting/concatenation—illustrated with concrete code examples and step‑by‑step explanations.
The article outlines key mathematical areas—probability & statistics, linear algebra, calculus, information theory, and convex optimization—essential for deep learning, and recommends specific lecture notes and textbooks for each topic to guide learners toward effective study.
This article breaks down the essential mathematics—linear algebra, probability, calculus, and optimization—required for mastering deep learning, explains how each topic maps to core deep‑learning concepts, and outlines six progressive learning stages with concrete examples and recommended textbooks.
From foundational concepts to practical examples, this guide walks engineers through ten essential supervised and unsupervised machine‑learning algorithms—decision trees, Naïve Bayes, linear regression, logistic regression, SVM, ensemble methods, clustering, PCA, SVD, and ICA—explaining their theory, real‑world uses, and why they matter.
