Gleam Lab · Blog Archive
Blog Page 12
Technical exploration and engineering notes, 655 articles in total.
AI Research 09 - Running Analysis and Research: Optimal Running Methods, Post-Run Nutrition and Body Recomposition
The optimal way to run is to keep heart rate between 60% and 80% of maximum heart rate. Post-run nutrition should include carbohydrates and protein promptly.
Tomcat Core Architecture Configuration: Server, Service, Executor and Engine
In-depth analysis of Tomcat core architecture covering detailed configuration of main tags including Server, Service, Executor, Engine with practical examples.
AI Research 07 - Running Benefits: Effects of 3K, 5K, 10K, Half Marathon and Marathon
Running has significant benefits for cardiovascular health, weight management, and psychological state.
AI Research 06 - The Physiological Health Effects and Mechanisms of Cold Showers
As a health intervention, cold showers can provide benefits such as refreshing, immune enhancement, and recovery promotion when practiced scientifically.
Tomcat Core Architecture: Catalina Container, Startup Flow and Threading Mechanism
In-depth analysis of Tomcat core architecture covering Catalina container full analysis, startup flow, and threading mechanism with comprehensive explanations.
AI Research 05 - The Physiological Health Effects and Mechanisms of Cold Showers: Cold vs Hot Showers
Cold showers have positive effects on mental health, including improved alertness, stress and depression relief, and enhanced psychological resilience.
Spring In-Depth: Tomcat Core Architecture, Coyote IO Model and Protocol
In-depth analysis of Tomcat core architecture and processing flow covering Coyote IO model and protocol with comprehensive explanations.
AI Research 04 - The Physiological Health Effects and Mechanisms of Cold Showers Part 1
Cold showers typically refer to showering with water temperature at or below 20°C, with positive effects on immune function, blood circulation, metabolic rate...
Spring In-Depth: Nginx Process Mechanism, Master-Worker Coordination and Common Commands
In-depth analysis of Nginx underlying process mechanism covering Master Worker mechanism principles and commonly used commands with practical examples.
AI Research 03 - Does Technical Time Investment Correlate with Salary Returns?
Reasons for stagnant income despite accumulated technical capabilities include position saturation, increased talent supply.
Spring In-Depth: Nginx Basic Config, Events/HTTP, Reverse Proxy and Load Balancing
In-depth guide to Nginx configuration covering nginx.conf structure, Events block, HTTP block, reverse proxy, and load balancing with practical examples.
AI Research 02 - Does Technical Time Investment Correlate with Salary Growth? Part 1
Technical investment and income are generally positively correlated, but returns do not grow infinitely.
Spring In-Depth: Nginx Introduction, History, Scenarios and Quick Start
In-depth introduction to Nginx covering its origins, development history, common scenarios, and quick configuration guide with practical examples.
Spring In-Depth: Declarative Transaction Support, XML and XML+Annotation Modes
In-depth guide to Spring declarative transaction support covering transaction configuration with XML mode and XML+annotation mode with practical examples.
Spring In-Depth: AOP Aspect Enhancement Core Concepts, Advice Types and XML + Annotation
In-depth introduction to Spring AOP aspect enhancement covering core concepts, advice types, XML+annotation approaches with code examples
Spring In-Depth: Declarative Transaction Support, Transaction Control and Isolation Levels
In-depth introduction to Spring declarative transaction support covering transaction control, concepts, four characteristics, and isolation levels with practical examples.
Big Data 278 - Spark MLlib GBDT Case Study: Residuals, Regression Trees & Iterative Training
GBDT practical case study walking through the complete process from residual calculation to regression tree construction and iterative training.
Spark MLlib: Bagging vs Boosting Differences and GBDT Gradient Boosting
Introduces the differences between Bagging and Boosting in machine learning, and the GBDT (Gradient Boosting Decision Tree) algorithm principles.
Spark MLlib GBDT Algorithm: Gradient Boosting Principles,and Applications
This article introduces the principles and applications of gradient boosting tree (GBDT) algorithm.
Spark MLlib Ensemble Learning: Random Forest, Bagging and Boosting Methods
This article systematically introduces ensemble learning methods in machine learning.