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aop-thinking

Aspect-Oriented Programming (AOP) complements Object-Oriented Programming (OOP) by providing another way of thinking about program structure. The key unit of modularity in OOP is the class, whereas in AOP the unit of modularity is the aspect. Aspects enable the modularization of concerns such as transaction management that cut across multiple types and objects.

artificial-intelligence-roadmap-thinking

handwritten-digit-classification-thinking

The MNIST handwritten digit classification problem is a standard dataset used in computer vision and deep learning. Although the dataset is effectively solved, it can be used as the basis for learning and practicing how to develop, evaluate, and use convolutional deep learning neural networks for image classification from scratch. This includes how to develop a robust test harness for estimating the performance of the model, how to explore improvements to the model, and how to save the model and later load it to make predictions on new data.

iceoryx-thinking

kalmanfilter-thinking

kalmanfilter-thinking

logging-ecosystem-thinking

Spring is using Java Commons Logging by default, and the JCL has a runtime discovery algorithm to find out for other logging frameworks in well known places on the project classpath.

mybatis-thinking

MyBatis is a first class persistence framework with support for custom SQL, stored procedures and advanced mappings. MyBatis eliminates almost all of the JDBC code and manual setting of parameters and retrieval of results. MyBatis can use simple XML or Annotations for configuration and map primitives, Map interfaces and Java POJOs (Plain Old Java Objects) to database records.

object-detection-thinking

Object detection is a computer vision technique for locating instances of objects in images or videos. Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results. When humans look at images or video, we can recognize and locate objects of interest within a matter of moments. The goal of object detection is to replicate this intelligence using a computer.

pengfeinie.github.io

https://pengfeinie.github.io/

regularization-deep-learning-thinking

A central problem in machine learning is how to make an algorithm that willperform well not just on the training data, but also on new inputs. Many strategiesused in machine learning are explicitly designed to reduce the test error, possiblyat the expense of increased training error. These strategies are known collectivelyas regularization. A great many forms of regularization are available to the deeplearning practitioner. In fact, developing more effective regularization strategieshas been one of the major research efforts in the field.

ros-thinking

spi-thinking

A feature for discovering and loading implementations matching a given interface: Service Provider Interface (SPI).

spring-circular-reference-thinking

When having a circular dependency, Spring cannot decide which of the beans should be created first, since they depend on one another. In these cases, Spring will raise a BeanCurrentlyInCreationException while loading context.

spring-integration-thirdparty-thinking

How to Integrate Spring with third party framework ?

springmvc-history-thinking

When using Spring in a web application, we have several options for organizing the application contexts that wire it all up. In this article, we're going to analyze and explain the most common options that Spring offers.

srpingboot-zero-config-thinking

Do you know how to mock spring boot zero configuration? If you’re looking for information about this course , please see this project.

thinking-in-arm

thinking-in-cpp

thinking-in-llm

Inference code for CodeLlama models

thinking-in-pcie

thinking-in-tbox

two-way-ssl-authentication-thinking

we’ll focus on the main use cases for X.509 certificate authentication – verifying the identity of a communication peer when using the HTTPS (HTTP over SSL) protocol.