DNS resolution in depth
Let’s walk through the scenario where you bought a domain from GoDaddy, host your website content on host.com, and map your domain to the website content. 1. You Buy a Domain from GoDaddy 2....
Author: marjavamitjava
Handling Character Encoding in Java: Ensuring Accurate Text Representation
Character encoding issues are a common challenge when working with text data in software development. These issues often arise when data is read from or written to streams and files, especially when dealing with...
Optimizing Solr Client Usage by Caching for Each Collection
Introduction When working with Apache Solr, especially in applications with multiple collections, it’s common to create a new Solr client instance for each connection. While this may seem straightforward, it can lead to performance...
Understanding Lua Scripts in Redis: Keys and Non-Key Values
Redis provides powerful scripting capabilities through Lua, enabling you to execute complex logic directly within the database. When working with Redis Cluster, it’s essential to understand how to properly pass keys and non-key values...
How To Solve OutOfMemoryError: Java Heap Space
Sample Program Generating ‘OutOfMemoryError: Java heap space’ To better understand java.lang.OutOfMemoryError: Java heap space, let’s try to simulate it. Let’s leverage BuggyApp, a simple open-source chaos engineering project. BuggyApp can generate various sorts of performance problems...
Java Garbage Collection: Comparing Serial, Parallel, CMS, G1, ZGC, and Shenandoah
Here are detailed descriptions of the different types of Java garbage collectors, including some technical differences: 1. Serial Garbage Collector (Serial GC) 2. Parallel Garbage Collector (Parallel GC) 3. Concurrent Mark-Sweep (CMS) Garbage Collector...
Java Classloaders: A Deep Dive
Understanding the Basics Before delving into the complexities, let’s establish a foundation. Beyond the Basics Class Loader Lifecycle Custom Class Loaders
Understanding Convolutional Layers: Forward and Backward Passes with Multiple Filters
In actual convolutional neural networks (CNNs), multiple filters are used in each convolutional layer. Each filter learns to detect different features in the input data. Here’s a more detailed explanation of how this works:...
Convolutional Layers in AI Model Training: A Deep Dive into 3×3 Filters
A Convolutional Layer is a fundamental building block of Convolutional Neural Networks (CNNs), primarily used in image processing tasks such as image classification, object detection, and image segmentation. This layer applies convolution operations to...
Training an AI model to classify images
Training an AI model to classify images into categories like cat, dog, or lion involves several mathematical steps, from preprocessing the images to training the neural network. Here’s a detailed breakdown of the process:...